1,721,076 research outputs found
[Conference version] Effects of face-to-face interaction on motivations to participate in technology-mediated citizen science
No full textCitizen science is a popular means of engaging the general public in research activities led by professional scientists. By involving a large number of these amateur scientists, citizen science offers the advantages of distributed data collection and analysis on a scale that would be otherwise difficult and costly to achieve. While advancements in information technology in the past decades have fostered the growth of citizen science through online participation, continuous recruitment and engagement of participants remains important factors. Such web-based projects may alienate the citizen scientists from the researchers.
In this paper, we investigate how motivations to participate in a citizen science project vary after an on-site, face to face interaction with the scientists leading the project. We use a citizen science-based environmental monitoring project as a case study, and in a measure-manipulate-measure experiment, find that involving users directly in data collection and interaction with the researchers of the project increases overall participant motivation to contribute to the project. Cultural and societal factors that contribute to motivation are also dissected and analyzed. Our findings provide an exploratory insight into a means for better motivating contributors and predicting who would be more positively affected to contribute. The subsequent benefits to data collection and analysis for environmental monitoring are immediate. Moreover, the increased scientific literacy of contributors as a result of raised participation, let citizen scientists represent a useful resource to be involved by public and private managers in other crowd-based projects as wise and technological prepared participants
agency expectancy efficacy
No full textBackground: an ongoing debate in HCI concerns what HCI design should prioritize - transparency, value
to the users, or other goals? There’s literature on the many cases where design acts in the interest of the
user, but in a way that is not fully transparent to the user, or one that makes value judgment on what
would benefit the user (E.g. Adar - benevolent deception , Vaccaro et al).
Within this context, we focus on the design of AI-based medical advice. New tools featuring AI-based medical advice are becoming increasingly available either directly to patients, or as an aid to a provider
who gives advice to the patient. The design of systems and interfaces that provide advice embodies,
knowingly to the designer or not, values and assumptions on what users should experience [reference].
From an ethical perspective, the design may follow a deontological approach (which emphasizes what the
user’s experience and actions ought to be, regardless of the consequences of the user's actions), or a
consequentialist approach (which emphasizes what the consequences of the user’s experience and
actions’ ought to be). Design emphasizing one or the other is likely to lead to different user experiences
and behaviors.
In this study, we consider two stakeholders in an advice setting:
Advice recipient (a patient),
Advice designer (a designer of an algorithmic advice system)
For advice to have an effect, the advice recipient needs to follow it. We can therefore define: Advice Effect
Expectancy for patient i = f(efficacy of the advice, likelihood that the advice recipient will follow the advice).
In many cases users will follow the most efficacious advice. But what happens when the designer knows
(based on historical and user data, sometimes through machine learning) that an advice recipient is very
likely not to follow the most efficacious course of action? A design dilemma is therefore: RQ1: Which of the
following should advice designers prioritize?
Efficacy: advice that prioritizes what is most efficacious if followed by the advice recipient,
regardless of how likely the advice recipient is to follow it, or
Advice effect expectancy (consequentialist approach): advice that maximizes the expectancy of the
advice’s effect - this approach prioritizes the combined effect of the efficacy and the likelihood that
the advice recipient will follow the advice.
Agency (deontological approach): advice that prioritizes agency - provides all known information to
the user, including treatment options and likelihood of advice following.
Guided agency (focus on moving people past indecision): advice that provides all known
information to the user, including treatment options and likelihood of advice following, and in
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addition, the provider’s recommendation of which of treatment options the provider thinks would be
most appropriate for the patient.
How people - both designers and users - respond to these design dilemmas is likely to be influenced by
multiple factors. We ask: RQ2: what are the moderating effects of:
The cost to the advice receiver of not following the advice (e.g., cost in health impact (provide
details)
Whether the advice is coming from a human vs. not (e.g. an AI system).
Scenarios (in each of them, the options should be mutually exclusive):
Adherence vs. lack of adherence to medical recommendation: option A (optimal): take a pill every
day, but if a day is missed, the treatment is worthless; option B (suboptimal): give a one time pill at
the doctor’s office; both are better than no advice.
The questionnaire will capture 2 perspectives, asking participants about
Which advice they would prioritize as an advice designer
Which advice they would prioritize as an advice recipient
Each participant will be asked only one of these.
These design dilemmas reflect human values and how these values should be baked into system design,
reflecting the perspectives of both designers and users
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Informing and Improving Retirement Saving Performance using Behavioral Economics Theory-driven User Interfaces
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