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Record W7134870948

The High Costs of Costs: Fees as Barriers to Access within the United States and Canadian Freedom of Information Régimes

2009· article· W7134870948 on OpenAlexaboutno aff
Zachary Pall

Bibliographic record

Venuenot available
Typearticle
Language
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsFreedom of informationGovernment (linguistics)Balance (ability)Public accessAccess to informationResource (disambiguation)Public policy
DOInot available

Abstract

fetched live from OpenAlex

The article argues that the fee structures under the United States Freedom of Information Act (FOIA) and the Canadian Access to Information Act create significant barriers to accessing government records, undermining the principle of transparency. While both systems aim to balance access with resource management, the U.S. system is particularly criticized for its complexity and inefficiency, which discourages requesters and fails to serve the public interest. The Canadian system, though flawed, offers a more streamlined approach that could provide valuable lessons for reforming the U.S. system.

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)0.005
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.119
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0160.037
Scholarly communication0.0250.011
Open science0.0020.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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