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Record W2149364466 · doi:10.1145/2675133.2675162

Designing for Discomfort

2015· article· en· W2149364466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransformative learningFeelingCraftReflection (computer programming)Process (computing)PsychologyFocus groupWork (physics)Computer sciencePedagogyEngineeringSocial psychologySociology

Abstract

fetched live from OpenAlex

A focus of human-computer interaction work and a central principle of user experience is that design should avoid discomfort and aim to craft positive experiences for individuals. However, for contexts in which an uncomfortable reaction is intended, instrumental, or indeed inevitable, we recognize that it is inappropriate to design for a positive or "feel good" experience. Herein we describe an investigation into the use of interactive technologies to support transformative learning, a process through which individuals engage with feelings of discomfort. The project is grounded by work with graduate students enrolled in a course that employed decolonizing pedagogies. Throughout the course students responded to uncomfortable, problematic scenarios through interactive tools. We present our analysis of students' learning experiences, their interactions with technologies and their reflections on the effectiveness of these engagements in terms of supporting opportunities for critical reflection, a crucial stage of the transformative learning process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.398
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.305
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations36
Published2015
Admission routes1
Has abstractyes

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