MétaCan
Menu
Back to cohort

Testing Alternative Theories of Agenda Setting: Forest Policy Change in British Columbia, Canada

2000· article· en· W1975645076 on OpenAlexaboutno aff
Sheldon Kamieniecki

Bibliographic record

VenuePolicy Studies Journal · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Political sciencePoliticsPublic administrationEnvironmental policySustainable forest managementPublic policyEnvironmental resource managementEnvironmental planningForest managementGeographyForestryEconomicsLaw

Abstract

fetched live from OpenAlex

In an effort to add to our understanding of why government chooses to take actions in certain situations and not others, this article applies competing theories of agenda building to a specific environmental issue, forest policymaking in British Columbia (BC), Canada. Clearly, how well BC and the rest of Canadian provinces manage their forests and other natural resources will contribute to the ability of Canada as a whole to become a sustainable society. In the 1990s the BC government enacted the Forest Practices Code, a comprehensive approach to managing the province's forests. This study relies on several different theoretical approaches to explain how such an ambitious program was developed and implemented. How difficult and complex environmental issues manage to reach the political agenda is crucial to our understanding of policy change and is addressed in this study.

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)0.012
metaresearch head score (Gemma)0.048
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0200.011
Scholarly communication0.0100.004
Open science0.0040.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.000

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 head0.070
GPT teacher head0.374
Teacher spread0.304 · 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

Citations31
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePolicy Studies JournalSame topicPolicy Transfer and LearningFrench-language works237,207