MétaCan
Menu
Back to cohort
Record W2162394302 · doi:10.1177/0002716213492633

Constructing Consequences for Noncompliance

2013· article· en· W2162394302 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

VenueThe Annals of the American Academy of Political and Social Science · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsAccountabilityCorporate governancePublic relationsWork (physics)BusinessMargin (machine learning)Face (sociological concept)Political scienceEngineering ethicsSociologyComputer scienceLawEngineering

Abstract

fetched live from OpenAlex

We examine academic research laboratories as examples of intractable governance sites. These spaces often elude regulatory warnings and rules because of the professional status of faculty members, the opacity of scientific work to outsiders, and loose coupling of policy and practice in organizations. We describe one university’s efforts to create a system for managing laboratory health, safety, and environmental hazards, thereby constraining conventional faculty habit to ignore administrative and legal procedures. We demonstrate the specific struggles safety managers face in creating system responsiveness, that is, feedback to re-channel noncompliant laboratory practices. We show how faculty members are buffered from the consequences of their activities, thus impeding the goals of responsibility and accountability. We conclude by asking where such pockets of intractability reside in other organizations and whether the surrounding buffer, if there is one, may nonetheless paradoxically create an effective margin of safety.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.233
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.025
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 head0.452
GPT teacher head0.532
Teacher spread0.079 · 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