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Record W4400778471 · doi:10.1561/112.00000579

The Impact of Forest Policy Modernization in British Columbia: A Stock Market Perspective

2024· article· en· W4400778471 on OpenAlexaboutno aff
Kurt Niquidet, Kyle Sia-Chan, Jonathan Kan, Daowei Zhang

Bibliographic record

VenueJournal of Forest Economics · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsModernization theoryPerspective (graphical)Stock marketStock (firearms)EconomicsNatural resource economicsForestryAgricultural economicsGeographyEconomic growthArchaeology

Abstract

fetched live from OpenAlex

This paper examines the impact of a modernized forest policy change in 2021 on the stock prices of forest products firms that operate and hold tenure in British Columbia. Our results suggest that the announcement of new forest policies led to substantial losses in the stock prices of five major publicly traded forest products firms that operate in the province. As a control, we also found that another public company that operated in another Canadian province and was not subject to the policy changes experienced an insignificant impact on their stock price. The impact on market capitalization is related to the harvesting rights held by these companies within British Columbia under various forest tenures. The results highlight that, for a variety of reasons, investors thought the proposed policy changes would adversely impact each firm’s future financial performance.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.244
Teacher spread0.238 · 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
Published2024
Admission routes1
Has abstractyes

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