MétaCan
Menu
Back to cohort
Record W2026041722 · doi:10.1111/0033-3352.00093

Less Government, More Secrecy: Reinvention and the Weakening of Freedom of Information Law

2000· article· en· W2026041722 on OpenAlexaffabout
Alasdair Roberts

Bibliographic record

VenuePublic Administration Review · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicOmbudsman and Human Rights
Canadian institutionsQueen's University
Fundersnot available
KeywordsFreedom of informationSecrecyAccountabilityTransparency (behavior)RestructuringDemocracyBusinessGovernment (linguistics)PoliticsPublic administrationPublic relationsLaw and economicsPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

Many critics have suggested that worldwide efforts to reinvent government could also weaken democratic control over public institutions, but few have considered how attempts to implement the “new paradigm” in public management might affect a widely used instrument for promoting accountability: freedom of information law (FOI). FOI laws give citizens and nongovernmental organizations the right of access to government information. However, recent Canadian experience shows that reinvention can weaken FOI laws in three ways. First, attempts to reduce “nonessential” spending may cause delays in handling FOI requests and weaken mechanisms for ensuring compliance. Second, governmental functions may be transferred to private contractors and not‐for‐profit organizations that are not required to comply with FOI laws. Third, governments' attempts to sell information and increase FOI fees may create new economic barriers to openness. Thus, restructuring provides an opportunity for political executives, public servants, and some well‐organized business interests to weaken oversight mechanisms and increase their own autonomy within the policy process.

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)0.039
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.046
Scholarly communication0.0090.013
Open science0.0020.006
Research integrity0.0060.007
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 head0.030
GPT teacher head0.299
Teacher spread0.268 · 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 designNot applicable
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

Citations14
Published2000
Admission routes2
Has abstractyes

Explore more

Same venuePublic Administration ReviewSame topicOmbudsman and Human RightsFrench-language works237,207