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Record W2951947220 · doi:10.1145/3325480.3326567

Unpacking the Thinking and Making Behind a Slow Technology Research Product with Slow Game

2019· article· en· W2951947220 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsEmily Carr University of Art and DesignSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsUnpackingSlownessArtifact (error)CreativityComputer scienceProcess (computing)Game designKey (lock)Product (mathematics)Product designHuman–computer interactionWork (physics)Knowledge managementPsychologySocial psychologyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Motivated by prior work on everyday creativity, we adopt a design-oriented approach seeks to move beyond designing for explicit interactions to also include the implicit, incremental and, at times even, unknowing encounters that slowly emerge among people, technologies, and artifacts over time. We contribute an investigation into designing for slowness grounded in the practice of making a design artifact called Slow Game. We offer a detailed critical-reflective accounting of our process of making Slow Game into a research product. In attending to key design moves across our process, we reveal hidden challenges in designing slow technology research products and discuss how our findings can be mobilized in future work.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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