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Record W4213291282 · doi:10.2139/ssrn.3987388

Comments on the Toronto Police Services Board Proposed Policy on AI Technologies - Montréal Society and Artificial Intelligence Collective (MoSAIC)

2021· article· en· W4213291282 on OpenAlexaffabout
Ana Brandusescu, Alan Chan, Fernando Aranguren Díaz, Andrés Ferraro, Alex Ketchum, Fenwick McKelvey, Jimin Rhim, Shalaleh Rismani, Renée Sieber, Jonathan Sterne, Yuan Stevens

Bibliographic record

VenueSSRN Electronic Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsUniversity of OttawaConcordia UniversityUniversité de MontréalToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsMosaicCollective intelligenceArtificial intelligenceSociologyEngineeringManagementPolitical scienceOperations researchPublic administrationComputer scienceArtEconomicsVisual arts

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.979
Threshold uncertainty score0.790

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.045
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0210.007
Scholarly communication0.0160.006
Open science0.0060.005
Research integrity0.1440.060
Insufficient payload (model declined to judge)0.0360.010

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 head0.024
GPT teacher head0.324
Teacher spread0.300 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractno

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Same venueSSRN Electronic JournalSame topicArtificial Intelligence in LawFrench-language works237,207