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Record W2610222447 · doi:10.1111/lapo.12080

Brokering Access Beyond the Border and in the Wild: Comparing Freedom of Information Law and Policy in Canada and the United States

2017· article· en· W2610222447 on OpenAlexafffundabout
Alex Luscombe, Kevin Walby, Randy K. Lippert

Bibliographic record

VenueLaw & Policy · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFreedom of informationState (computer science)Meaning (existential)Political scienceLawVariation (astronomy)Public administrationPublic policyPublic accessPsychology

Abstract

fetched live from OpenAlex

Contributing to literature on jurisdictional variation in freedom of information (FOI) law and policy, we draw from accounts of experiences of FOI requests submitted to police agencies in nine Canadian provinces and ten US states. We conceptualize these experiences using notions of “brokering access,” “law in the wild,” and “feral law.” Our findings demonstrate key differences in how public police agencies store, prepare, and disclose information at municipal and provincial/state levels in Canada and the US, meaning that FOI‐related feral lawyering in Canada and the United States differs and fluctuates because of the variation in the mode of contact with FOI coordinators, fee estimate practices, and procedures for and responsiveness to appeals. In conclusion, we discuss the implications of our findings for methodological and sociolegal literature about FOI requests and for provincial/state FOI policies in both countries.

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.

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)0.006
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.010
Science and technology studies0.0300.019
Scholarly communication0.0140.004
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations29
Published2017
Admission routes3
Has abstractyes

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