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Record W2154086492 · doi:10.1139/x07-072

Pricing the social contract in the British Columbian forest sector

2007· article· en· W2154086492 on OpenAlexaffvenue
Kurt Niquidet, Harry W. Nelson, Ilan Vertinsky

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomic rentRevenueBusinessDistribution (mathematics)Natural resource economicsGovernment (linguistics)Value (mathematics)Agricultural economicsTerm (time)EconomicsFinanceMarket economy

Abstract

fetched live from OpenAlex

In this paper, we investigate the impact of various socioeconomic conditions on the value of timber tenures in the province of British Columbia. Two timber tenure models were created, one for short-term timber sale licenses and the other for longer term forest licenses. The short-term model revealed that timber sales that were awarded according to a combination of employment, revenue, and manufacturing criteria yielded $8.63/m3 less revenue than timber sales awarded based on revenue alone. Similarly, the long-term model indicates that manufacturing and employment conditions significantly reduce the bid on forest licenses. In both instances, we suggest that such conditions distort the use of timber, labour, and capital. Therefore, we conclude that recent forest policy changes in the province that removed several of these conditions greatly improved economic efficiency. Nevertheless, distribution impacts are likely to be important because resource rents have potentially been redistributed away from rural communities to the provincial government.

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)0.008
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 score0.148
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.039
GPT teacher head0.314
Teacher spread0.276 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

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