MétaCan
Menu
Back to cohort
Record W2095527295 · doi:10.5558/tfc81081-1

New Brunswick's "Jaakko Pöyry" report: perceptions of senior forestry officials about its influence on forest policy

2005· article· en· W2095527295 on OpenAlexaffvenueabout
Wm. Ashton, Bill Anderson

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGovernment (linguistics)Public policyForestryPublic administrationPerceptionForest industryPolitical scienceProcess (computing)BusinessPolicy makingGeographyPsychology

Abstract

fetched live from OpenAlex

Pending limited fibre supplies in New Brunswick are of concern to the forest industry, provincial government departments, and special interest groups, alike. All three of these stakeholders employ foresters, and all three are or should be involved in setting public policy regarding New Brunswick's forests. This paper uses a multifaceted framework to assess the role of foresters in the current policy debate regarding softwood fibre supplies that has resulted from New Brunswick's "Jaakko Pöyry" report. The conclusions from this study are that i) both forest policy and the policymaking process in New Brunswick have largely been determined by industry- and government-commissioned reports; ii) the policy-making process remains undefined; and iii) all the stakeholders see a need to improve communications. Key words: forest policy; Jaakko Pöyry report; New Brunswick; perceptions; public policy making.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.986
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0050.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.272
Teacher spread0.261 · 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

Citations5
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueThe Forestry ChronicleSame topicForest Management and PolicyFrench-language works237,207