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Record W3157216313 · doi:10.1145/3449242

Becoming Interdisciplinary

2021· article· en· W3157216313 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

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEngineering ethicsProcess (computing)CriticismPosition paperEvent (particle physics)SociologyData scienceManagement scienceComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

ICTs such as mapping platforms, algorithms, and databases are a central component of how society responds to the threats posed by disasters. However, these systems have come under increasing criticism in recent years for prioritizing technical disciplines over insights from the humanities and social science and failing to adequately incorporate the perspectives of at-risk or affected communities. This paper describes a unique month-long workshop that convened interdisciplinary experts to collaborate on projects related to flood data. In addition to findings about the practical accomplishment of interdisciplinary collaboration, we offer three interrelated contributions. First, we position interdisciplinarity as a critical practice and offer a detailed example of how we staged this process. We then discuss the benefits to interdisciplinarity of expanding the range of temporal logics normally deployed in design workshops. Finally, we reflect on approaches to evaluating the event's contributions toward sustained critique and reform of expert practice.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.006
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 head0.064
GPT teacher head0.356
Teacher spread0.292 · 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