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Experiences of Public Toilets 2023
No full textAineisto koostuu kirjoituksista, joissa vastaajat kertovat kokemuksistaan julkisista vessoista. Vastaajilla on jokin suoliston toimintaan vaikuttava sairaus, vamma tai muu rajoite. Kirjoituskeruun tavoitteena oli muun muassa saada tietoa vessaan pääsemiseen liittyvistä epäkohdista sekä pyrkiä vähentämään ulostamiseen liittyviä epätasa-arvoisuuksia ja ennakkoluuloja. Kirjoitusohjeissa vastaajia pyydettiin kertomaan vapaamuotoisesti kokemuksistaan julkisista vessoista. Lisäksi oltiin kiinnostuneita siitä, onko julkisiin vessoihin helppo päästä ja miten julkisia vessoja tulisi kehittää sekä mikä julkisissa vessoissa toimii ja mikä ei. Myös siitä, miten vastaajien tulee valmistautua lähtiessään asioimaan kodin ulkopuolelle, oltiin kiinnostuneita. Edelleen kysyttiin julkisten vessojen puutteiden synnyttämiin arjen rajoitteisiin sekä vastaajien kohtaamiin ennakkoluuloihin ja kulttuuriseen ilmapiiriin liittyen vessassa käymiseen. Lopuksi pyydettiin vielä vastaajia ideoimaan täydellinen julkinen vessa. Taustatietoina aineistossa on sukupuoli, ikäryhmä sekä tieto siitä, mikä suoliston toimintaan liittyvä vaiva vastaajalla on. Aineistosta on tehty html-hakemisto, jonka hakemiston avulla kirjoituksia on helppo selata.The data consists of writings in which respondents describe their experiences of public toilets. Respondents have a disease, disability or other limitation affecting bowel function. The aim of the collection was, among other things, to raise awareness of the inequalities in access to public toilets and to reduce inequalities and prejudices related to defecation. In the instructions, respondents were asked to describe their experiences of public toilets in a free-form format. There was also an interest in whether public toilets were easily accessible, how public toilets should be developed and what works and what doesn't work in public toilets. Also, it was asked how respondents need to prepare themselves when leaving home. Further questions were asked about the everyday constraints created by the lack of public toilets and the prejudices and cultural climate faced by respondents when using the toilet. Finally, respondents were asked to come up with an idea for the perfect public toilet. The background data include gender, age group and information on what bowel-related condition the respondent has. The data were organised into an easy to use HTML version at FSD
English Housing Survey, 2023-2024: Household Data: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The English Housing Survey (EHS) is a continuous national survey commissioned by the Department for Communities and Local Government (DCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England. The EHS brings together two previous survey series into a single fieldwork operation: the English House Condition Survey (EHCS) (available from the UK Data Archive under GN 33158) and the Survey of English Housing (SEH) (available under GN 33277). The EHS covers all housing tenures and provides valuable information and evidence to inform the development and monitoring of the department's housing policies. Results from the survey are also used by a wide range of other users including other government departments, local authorities, housing associations, landlords, academics, construction industry professionals, consultants, and the general public. The EHS has a complex multi-stage methodology consisting of two main elements; an initial interview survey of around 14,000 households and a follow-up physical inspection. Some further elements are also periodically included in or derived from the EHS: for 2008 and 2009, a desk-based market valuation was conducted of a sub-sample of 8,000 dwellings (including vacant ones), but this was not carried out from 2010 onwards. A periodic follow-up survey of private landlords and agents (the Private Landlords Survey (PLS)) is conducted using information from the EHS interview survey. Fuel Poverty datasets are also available from 2003, created by the Department for Energy and Climate Change (DECC). The EHS interview survey sample formed part of the Integrated Household Survey (IHS) (available from the Archive under GN 33420) from April 2008 to April 2011. During this period the core questions from the IHS formed part of the EHS questionnaire. Safeguarded and Special Licence Versions:From 2014 data onwards, the Safeguarded versions (previously known as End User Licence (EUL)) of the EHS will only include derived variables. In addition the number of variables on the new EUL datasets has been reduced and disclosure control increased on certain remaining variables. New Special Licence versions of the EHS will be deposited later in the year, which will be of a similar nature to previous EHS EUL datasets and will include derived and raw datasets. Further information about the EHS and the latest news, reports and tables can be found on the GOV.UK English Housing Survey web pages. SN 9444 - English Housing Survey, 2023-2024: Household Data: Special Licence Access contains data from the interview survey only. The data from the physical survey are available under SN 9443 - English Housing Survey, 2023: Housing Stock Data: Special Licence Access.Main Topics:The EHS Housing survey consists of two components.Interview survey on the participating household - An interview is first conducted with the householder. The interview topics include: household characteristics, satisfaction with the home and the area, disability and adaptations to the home, ownership and rental details and income details. All interviewees are guaranteed confidentiality and all data is anonymised.Physical survey on the housing stock - A visual inspection of both the interior and exterior of the dwelling is carried out by a qualified surveyor to assess the condition and energy efficiency of the dwelling. Topics covered include whether the dwelling meets the Decent Homes Standard; cost to make the dwelling decent; existence of damp and Category 1 Hazards as measured by the Housing Health and Safety Rating System (HHSRS); Energy Efficiency Rating. The physical survey is carried out on the dwelling of a sub-sample of the participants of the interview survey. The sub-sample consists of the dwelling of participants living in private or social rented properties and a sub-sample of those in owner occupied properties. A proportion of the dwellings found to be vacant during the interview survey are also included in the physical survey.</p
1970 British Cohort Study: Polygenic Indices, 2016-2018: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been surveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42, 46, and 51. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics. Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Polygenic IndicesPolygenic indices are available under Special Licence SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.Secure Access datasetsSecure Access versions of BCS70 have more restrictive access conditions than versions available under the standard Safeguarded Licence.SN 9439 - 1970 British Cohort Study: Polygenic Indices, 2016-2018: Special Licence AccessPolygenic indices (PGIs) aggregate Genome-wide Association Studies (GWAS) estimates across all measured single nucleotide polymorphisms (SNPs) to provide a single estimate of an individual’s genetic predisposition towards the trait under study. As such, SNPs can be considered as the building blocks of PGIs. It is important to note that the genetic predisposition represented by PGI is known inasmuch as it has been estimated accurately and reliably from a GWAS; not all SNPs are included in the GWAS or the GWAS estimates for a given SNP are inaccurate, then the genetic predisposition represented by the PGI will be lower than the true genetic predisposition.The PGIs have been developed using a consistent methodology that has been applied to harmonised genetic data across each cohort, enabling researchers to engage in consistent cross-cohort analysis for using derived genetic measures for the first time. All PGIs have been derived from large scale Genome-wide Association Studies (GWAS) with publicly available summary statistics. This approach is hoped to enable and encourage wider use of the genetic data collected in these studies. High level guidance on the use and interpretation of PGIs is provided.The PGIs were also developed in a consistent manner in a birth cohort born in 1946 (MRC National Survey of Health and Development, 1946c), which can be obtained by separate application to the Unit for Lifelong Health and Ageing at UCL.Main Topics:Polygenic indices; polygenic scores; polygenic risk scores; genome-wide association studies; human genetics; anthropometrics; brain structure and cognition; health behaviours; mental health; personality; physical health; social outcomes.</p
Health Survey Northern Ireland, 2021-2022
No full textAbstract copyright UK Data Service and data collection copyright owner.The Health Survey Northern Ireland (HSNI) was commissioned by the Department of Health in Northern Ireland and the Central Survey Unit (CSU) of the Northern Ireland Statistics and Research Agency (NISRA) carried out the survey on their behalf. This survey series has been running on a continuous basis since April 2010 with separate modules for different policy areas included in different financial years. It covers a range of health topics that are important to the lives of people in Northern Ireland. The HSNI replaces the previous Northern Ireland Health and Social Wellbeing Survey (available under SNs 4589, 4590 and 5710).Adult BMI, height and weight measurements, accompanying demographic and derived variables, geography, and a BMI weighting variable, are available in separate datasets for each survey year. Further information is available from the Northern Ireland Statistics and Research Agency and the Department of Health (Northern Ireland) survey webpages. The Health Survey Northern Ireland, 2021-2022 provides a source of information on a wide range of health issues relevant to Northern Ireland. The information collected is pursuing the development of policies aimed at improving the health and well-being of the Northern Ireland population. Main Topics:Topics covered included: general health, long-term conditions, smoking, e-cigarettes, alcohol consumption, physical activity, mental health (including GHQ12), wellbeing, caring responsibility, loneliness, social support, sleep, care in the sun, stress and self-reported BMI.</p
Ofsted 'Big Listen' Exercise Data, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.Between 8 March 2024 and 31 May 2024, Ofsted carried out a ‘Big Listen’ exercise. The following information was provided to respondents, and summarises the purpose of the Big Listen exercise:"The Big Listen seeks views right across our work, from schools and children’s social care to teacher training and early years. Across our work, we want to explore 4 areas: reporting how we report on our education and regulatory inspectionsinspection practice the shape of our education and regulatory inspections, our ways of working and the craft of inspectingculture and purpose the conduct of our inspections and the way we workimpact the consequences of our inspections for children, professionals, institutions and parents' choices The Big Listen is structured to allow you to provide feedback on the areas of greatest interest and importance to you. You may only want to give us your views on schools. Or you might want to share views on how we report across further education and teacher training. Whatever your focus, we have structured the Big Listen so you only need to comment on the things that matter to you. Thank you for taking the time to help us improve and achieve our ambition of being a world-class inspectorate and regulator." The dataset available from UKDS contains individual level responses to all quantitative questions. Free text responses are not included.Further information can be found on the Ofsted Big Listen: Supporting Documents webpage. It is planned that a podcast based on the research will be released in due course.Main Topics:Early years; schools; further education (FE) and skills; teacher development, which includes initial teacher education (ITE), early career framework (ECF) and national professional qualification (NPQ) programmes; special educational needs and/or disabilities (SEND) and alternative provision; children's social care.</div
Growing Up in Scotland: Cohort 1: Sweep 7.5, 2013-2014: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Growing Up in Scotland (GUS) study is a large-scale longitudinal social survey which follows the lives of several groups of Scottish children from infancy through childhood and adolescence. It aims to provide important information on children, young people and their families in Scotland. The study forms a central part of the Scottish Government's strategy for the long-term monitoring and evaluation of its policies for children and young people, with a specific focus on the early years. The study seeks both to describe the characteristics, circumstances and experiences of children in their early years in Scotland and, through its longitudinal design, to generate a better understanding of how children's start in life can shape their longer term prospects and developmentSince 2005 fieldwork has been undertaken by the Scottish Centre for Social Research. The survey design for Birth Cohort 1 consisted of recruiting the parents of an initial total of 5,217 children aged 10 months old in 2005 and interviewing them annually until their child reached age six. Further fieldwork was then undertaken at ages 8, 10, 12, 14 and 17-18 with a sample boost added at age 12.Data for sweeps 1-9 were collected via an in-home, face-to-face interview with self-complete sections. Fieldwork for sweep 10 was disrupted due to the COVID pandemic. As a result, the final portion of the data was collected via web and telephone questionnaires. Sweep 11 data were gathered via web, telephone and face-to-face surveys of cohort members and their parent/carer.Further information about the survey may be found on the Growing Up in Scotland website.In May 20205, data and documentation for Cohort 1, Sweeps 1-11 were released as individual studies (SNs 9373-9383 and 9386-9387). Previously they were held under one study (SN 5760) which has been withdrawn from the data catalogue.Main Topics:The questionnaire covered several topics including:child's relationship with friends and parent/carerfamily functioningattitudes towards social issues and engagementA topic overview covering all sweeps, is available on the GUS website.</p
Quarterly Labour Force Survey Household Dataset, October - December, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.Household datasetsUp to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.Change to coding of missing values for household seriesFrom 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.Additional data derived from the QLFSThe Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.End User Licence and Secure Access QLFS Household datasetsUsers should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.Changes to variables in QLFS Household EUL datasetsIn order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.Review of imputation methods for LFS Household data - changes to missing valuesA review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders. Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.Main Topics:The LFS household datasets cover:characteristics of the household: number of people of working age; number of people over working age; number of children aged 0 to 4; number of children aged 5 to 15; number of dependent children (i.e. those in full-time education) aged 16 to 18economic activity in the household: number of people in employment; number of people in full-time employment; number of people in part-time employment; unemployed; economically inactive; students; sick or disabled; economically inactive but would like to work and are not seeking work because they do not believe there is work available ('discouraged workers'); care of dependants</ul
Gender and Adolescence: Global Evidence: Jordan UNICEF Cash Plus Endline, 2022-2023
No full textAbstract copyright UK Data Service and data collection copyright owner.Gender and Adolescence: Global Evidence (GAGE) is a ten-year (2015-2025) research programme, funded by UK Aid from the UK Foreign, Commonwealth and Development Office (FCDO), that seeks to combine longitudinal data collection and a mixed-methods approach to understand the lives of adolescents in particularly marginalized regions of the Global South, and to uncover 'what works' to support the development of their capabilities over the course of the second decade of life, when many of these individuals will go through key transitions such as finishing their education, starting to work, getting married and starting to have children.GAGE undertakes longitudinal research in seven countries in Africa (Ethiopia, Rwanda), Asia (Bangladesh, Nepal) and the Middle East (Jordan, Lebanon, Palestine). Sampling adolescent girls and boys aged between 10‐19‐year olds, the quantitative survey follows a global total of 18,000 adolescent girls and boys, and their caregivers and explores the effects that programme have on their lives. This is substantiated by in‐depth qualitative and participatory research with adolescents and their peers. Its policy and legal analysis work stream studies the processes of policy change that influence the investment in and effectiveness of adolescent programming.Further information, including publications, can be found on the Overseas Development Institute GAGE website. SN 9345 - Gender and Adolescence: Global Evidence: Jordan UNICEF Cash Plus Endline, 2022-2023In Jordan, GAGE initially recruited a sample of 4,095 adolescent girls and boys in two separate cohorts (younger adolescents aged 10-12 years and older adolescents age 15-17 years) during 2018 and 2019. This sample includes Syrian refugees living in refugee camps, informal tented settlements (ITS) and host communities, as well as Palestinian refugees living in refugee camps and host communities, vulnerable Jordanian adolescents living in communities hosting refugees, and a small group of adolescents of other nationalities living in Jordan.In collaboration with UNICEF, a new sample of 1,681 adolescents ages 10-18 years were recruited in 2021 as part of an evaluation of new UNICEF programming consisting of providing tablets (Treatment A) or tablets and cash (Treatment B) to existing participants in Makani, a type of youth centre. The sample includes Jordanians and Syrians at 19 Treatment A Makani centres and at 13 Treatment B centres. The Baseline UNICEF sample was followed-up in 2022-2023. This study contains Endline individual-level and the household-level data collected in 2022-2023. The Baseline survey data is available from SN 9344.Main Topics:Youth; adolescence; gender; longitudinal impact evaluation of youth programming.</p
Transatlantic Privacy Perceptions (TAPP), Wave 4 (Spring 2024)
No full textTAPP is an interdisciplinary research project conducted at the Universities of Maryland (UMD) and Munich (LMU). The privacy landscape is shaped by diverse actors from academia, policy, law, technology, journalism, and civil society, making it challenging to unify perspectives across sectors, regions, and cultural contexts. The Transatlantic Privacy Perceptions (TAPP) Panel addresses this by surveying influential privacy experts across jurisdictions with differing regulations. This survey captures stakeholders´ attitudes toward digital privacy, focusing on current and future challenges in privacy policies and practices. The focus of Wave 4 of the Transatlantic Privacy Perceptions (TAPP) Panel is on the use and governance of artificial intelligence (AI). This survey explores innovative applications of AI across various sectors and enhanced operational efficiencies. Additionally, significant emphasis is placed on capturing stakeholder knowledge and use of robust governance frameworks to ensure ethical use, transparency, and accountability in AI deployment. This approach aims to harness AI´s potential while mitigating risks and addressing societal concerns about bias, privacy, and the impact on employment. During the survey period from 17 April 2024 to 15 May 2024 privacy experts who deal with privacy in the United States and/or Europe were interviewed in online interviews (CAWI). Respondents were selected through a deliberate selection process based on their visibility, recognizability and influence in the field.Professional interest in privacy-related topics or both a personal and a professional interest in privacy-related topics; privacy as part of work; use of AI tools or systems as part of work; how much do privacy concerns affect respondent’s use of AI tools or systems in work; organization hast a framework or guidelines for using AI in the workplace; kind of organization’s frameworks and guidelines for AI use (internal, external, mixture of different frameworks); development of framework or guidelines by the organization or by another organization; organization plans the implementation of a framework or guidelines for future AI use; involvement in drafting the framework or guidelines; how does/ will the organization ensure that everyone complies with framework/ guidelines (individuals/ teams will be responsible for any necessary compliance, existing privacy work has/ will expand to include AI, a new role or team focused on AI, external consultants will monitor/ audit, do not know); feeling of confidence that the respondent/ the organization will be able to address privacy challenges that may arise when using AI tools or systems in work; how does the respondent/ the organization plan to address such challenges (no need to use AI systems, reviewing process, conducting assessment and defining necessary tools, use existing privacy tools (not adjusting for AI), use a mixture of external consultation and internal resources, using or creating AI framework); most important key aspects of practices centered around AI privacy (documentation and traceability of data sources, human involvement and/or oversight of AI decision-making, human-centered design (e.g., usability, explainability), technical privacy safeguards (e.g., encryption), privacy by design (e.g., default settings), data minimization, control over own data (e.g., access, correct, delete data), copyright protections, other, prefer not to answer); familiarity with Responsible AI principles; paid or voluntary privacy work (respondent performs privacy-related activities as part of his paid job vs. as a volunteer or activist); years of experience on privacy issues; sector for privacy work (academia, government, journalism, law, non-profit/NGO/ Think Tank, Tech industry, non-Tech industry, volunteer/ activist); region of expertise: policy context the respondent is most familiar with when it comes to privacy issues (United States, Europe, other). Additionally coded: Respondent ID, response ID, wave; returning respondent (individuals who have participated in at least one wave of TAPP); response status; willingness to participate in future waves of the TAPP Panel; date in which the survey was finished and recorded; duration to complete the survey (in seconds); country of expertise.TAPP ist ein interdisziplinäres Forschungsprojekt, das an den Universitäten von Maryland (UMD) und München (LMU) durchgeführt wird. Die Datenschutzlandschaft wird von verschiedenen Akteuren aus Wissenschaft, Politik, Recht, Technologie, Journalismus und Zivilgesellschaft geprägt, was es schwierig macht, Perspektiven über Sektoren, Regionen und kulturelle Kontexte hinweg zu vereinheitlichen. Das Transatlantic Privacy Perceptions (TAPP) Panel geht dieser Herausforderung an, indem es einflussreiche Datenschutzexperten aus verschiedenen Rechtsordnungen mit unterschiedlichen Vorschriften befragt. Diese Umfrage erfasst die Einstellungen der Interessengruppen zum digitalen Datenschutz und konzentriert sich dabei auf aktuelle und zukünftige Herausforderungen in der Datenschutzpolitik und -praxis. Der Schwerpunkt der vierten Welle des Transatlantic Privacy Perceptions (TAPP) Panels liegt auf der Nutzung und Steuerung künstlicher Intelligenz (KI). Diese Umfrage untersucht innovative Anwendungen von KI in verschiedenen Sektoren und verbesserte betriebliche Effizienz. Darüber hinaus wird großer Wert darauf gelegt, das Wissen der Interessengruppen und die Nutzung robuster Governance-Rahmenwerke zu erfassen, um eine ethische Nutzung, Transparenz und Rechenschaftspflicht beim Einsatz von KI sicherzustellen. Dieser Ansatz zielt darauf ab, das Potenzial der KI zu nutzen und gleichzeitig Risiken zu mindern und gesellschaftliche Bedenken hinsichtlich Voreingenommenheit, Datenschutz und Auswirkungen auf die Beschäftigung auszuräumen. Während des Erhebungszeitraums vom 17. April 2024 bis zum 15. Mai 2024 wurden Datenschutzexperten, die sich mit Datenschutz in den Vereinigten Staaten und/oder Europa befassen, in Online-Interviews (CAWI) befragt. Die Befragten wurden in einem sorgfältigen Auswahlverfahren auf der Grundlage ihrer Sichtbarkeit, Bekanntheit und ihres Einflusses in diesem Bereich ausgewählt.Berufliches Interesse an datenschutzrelevanten Themen oder sowohl persönliches als auch berufliches Interesse an datenschutzrelevanten Themen; Datenschutz als Teil der Arbeit; Einsatz von KI-Tools oder -Systemen als Teil der Arbeit; inwieweit beeinflussen Datenschutzbedenken die Nutzung von KI-Tools oder -Systemen durch die Befragten bei der Arbeit; verfügt die Organisation über einen Rahmen oder Richtlinien für den Einsatz von KI am Arbeitsplatz; Art der Rahmenbedingungen und Richtlinien der Organisation für den Einsatz von KI (intern, extern, Mischung aus verschiedenen Rahmenbedingungen); Entwicklung von Rahmenwerken oder Richtlinien durch die Organisation oder durch eine andere Organisation; Organisation plant die Umsetzung eines Rahmenwerks oder von Richtlinien für die zukünftige Nutzung von KI; Beteiligung an der Ausarbeitung des Rahmenwerks oder der Richtlinien; wie stellt die Organisation sicher, dass alle das Rahmenwerk/die Richtlinien einhalten (Einzelpersonen/Teams sind für die Einhaltung verantwortlich, bestehende Datenschutzmaßnahmen wurden/werden auf KI ausgeweitet, eine neue Rolle oder ein neues Team mit Schwerpunkt auf KI, externe Berater überwachen/prüfen, weiß nicht); Vertrauen, dass der Befragte/die Organisation in der Lage sein wird, Datenschutzprobleme zu lösen, die bei der Verwendung von KI-Tools oder -Systemen bei der Arbeit auftreten können; wie plant der Befragte/die Organisation, solche Herausforderungen anzugehen (keine Notwendigkeit, KI-Systeme einzusetzen, Überprüfung des Prozesses, Durchführung von Bewertungen und Definition der erforderlichen Tools, Nutzung bestehender Datenschutz-Tools (ohne Anpassung für KI), Nutzung einer Kombination aus externer Beratung und internen Ressourcen, Nutzung oder Erstellung eines KI-Rahmenwerks); wichtigste Aspekte der Praktiken im Zusammenhang mit dem Datenschutz bei KI (Dokumentation und Rückverfolgbarkeit von Datenquellen, menschliche Beteiligung und/oder Überwachung der KI-Entscheidungsfindung, menschenzentriertes Design (z. B. Benutzerfreundlichkeit, Erklärbarkeit), technische Datenschutzmaßnahmen (z. B. Verschlüsselung), Privacy by Design (z. B. Standardeinstellungen), Datenminimierung, Kontrolle über eigene Daten (z. B. Zugriff, Korrektur, Löschung von Daten), Urheberrechtsschutz, Sonstiges, keine Angabe); Vertrautheit mit den Grundsätzen der verantwortungsvollen KI; bezahlte oder freiwillige Datenschutzarbeit (der Befragte übt datenschutzbezogene Tätigkeiten im Rahmen seiner bezahlten Arbeit aus vs. als Freiwilliger oder Aktivist); Jahre Erfahrung mit Datenschutzfragen; Sektor für Datenschutzarbeit (Wissenschaft, Regierung, Journalismus, Recht, gemeinnützige Organisationen/NGOs/Think Tanks, Technologiebranche, Nicht-Technologiebranche, Freiwilliger/Aktivist); Kompetenzgebiet: politischer Kontext, mit dem der Befragte in Bezug auf Datenschutzfragen am besten vertraut ist (Vereinigte Staaten, Europa, Sonstiges). Zusätzlich verkodet: Befragten-ID, Antwort-ID, Welle; wiederkehrender Befragter (Personen, die an mindestens einer Welle von TAPP teilgenommen haben); Antwortstatus; Bereitschaft zur Teilnahme an zukünftigen Wellen des TAPP-Panels; Datum, an dem die Umfrage abgeschlossen und aufgezeichnet wurde; Dauer zum Ausfüllen der Umfrage (in Sekunden); Land der Expertise
Europolity - The politicization of European integration in the Greek public discourse, 1974-2019
No full textThe project analyzes the public discourse regarding the European integration process in Greece from 1974 until 2019. Using a political claims approach that combines political discourse with an analysis of attribution of responsibility and by using media data as the main source, the project will trace the politicization of European integration in Greece. The politicization of European integration that until some years ago was seen as absent is now widely accepted. Thus, EU politicization is mainly used in the relevant literature to describe more recent phenomena that indicate a change in patterns of support and opposition for European integration. Aiming to distinguish and compare the several styles of political debates on European integration in a national case study, we analyze these patterns of support and opposition from a long-term perspective. The analysis focuses and evolves around key events and critical thresholds (Treaties and agreements, European elections etc.) in the evolution of Greece’s accession to the European Economic Community (EEC) and later the country’s embedding in the European Union (EU). The claims-making approach brings to surface the actors (politicians, opinion-makers, civil society actors etc.) who appear in media reports and thoroughly analyzes their views. It also highlights the degree of Europeanization/transnationalization of the Greek public discourse since it shows to whom and at which spatial level the relevant claims are addressed each time. The overarching research question of the project is: What political claims are made and by whom in the Greek public sphere regarding the European integration process? However, the project does not limit itself to description but also explains the patterns of political claims-making in general and the attribution of responsibility in particular. Accordingly, the second set of research questions are: How and to what extent has public claim-making changed over the 1974-2019 period? How can we explain the different political claims-making by different actors? Our explanations refer to the structural position of the actors, the strategic interaction among them, and the political dynamic in Greece and Europe. Our findings relate to the ongoing debates on EU politicization, the debates on the Europeanization of the public sphere, and the Europeanization of contention, in light especially of the recent economic and refugee crises, the rise of negative critiques for the EU project, and the possible secession of member states. The project: combines the quantitative research method of political claim analysis in its special form of discursive attribution of responsibility analysis on media data with the qualitative research method of conducting semi-structured interviews with important actors in the public debate, complements the discursive approach with content analysis of selected parliamentary minutes over the 1975-2017 period, offers a policy analysis report of key EU related issues for Greece to enrich our understanding of the positioning of actors in the debate, the structure of available each time political opportunities and threats, and of the broader political and economic context. The project was initially hosted in the Department of Sociology and the Center for Research and Studies (KEME) at the University of Crete and then at the National Centre for Social Research – EKKE in Athens. Research conducted in close collaboration with the Center of Modern European Studies (CEMES) at the University of Copenhagen. The Principal Investigator was Dr. Kostas Kanellopoulos, and members of the research team were Dr. Angelos Loukakis, Dr. Stavros Protopapas, Mr. Konstantinos Kostopoulos (PhD candidate), Ms. Argyro Psychi-Fili (postgraduate student), and as non-salaried research associates: Prof. Maria Kousis (director of KEME), Dr. Giorgos Tsiolis and Mr. Nikos Kapelonis (all at the University of Crete) and Prof. Hans-Jӧrg Trenz (co-chair of CEMES at the University of Copenhagen and later Scuola Normale Superiore, Italy). The project received funding from the Hellenic Foundation for Research and Innovation (H.F.R.I Research Grant: 408)