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Record W1995547912 · doi:10.1139/x02-164

Estimating impacts of resource management policies in the Foothills Model Forest

2003· article· en· W1995547912 on OpenAlexvenueaboutno aff
Mike N. Patriquin, Janaki R.R. Alavalapati, Adam Wellstead, S. M. M. Young, Wiktor Adamowicz, William A. White

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsComputable general equilibriumFoothillsVisitor patternEconomicsEconomic impact analysisNatural resource economicsPolicy analysisScale (ratio)Resource (disambiguation)Forest managementEconomic modelEnvironmental resource managementForestryGeographyMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

This study examines the economic impact of policy changes in a forested region of the province of Alberta known as the Foothills Model Forest (FMF). The effects of policy changes in various sectors are analyzed in terms of all sectors of the regional economy with particular emphasis on the forestry sector. Unlike most analyses of regional policies, this study uses a computable general equilibrium (CGE) framework rather than a fixed-price framework to analyze economy-wide impacts of land use or forest policy changes. The application of this technique on a regional scale is rare in the scientific literature. Model results indicate that a decrease in forestry output somewhat offsets the positive economic impact generated by increased visitor activity. Failure to consider these trade-off impacts in the analysis will result in erroneous conclusions. The outcomes from three timely policy scenarios are examined in this paper. The results from the CGE framework suggest that policy makers face a greater degree of complexity than in current economic impact frameworks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.142
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.169
GPT teacher head0.295
Teacher spread0.125 · 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 teacher head, 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

Citations17
Published2003
Admission routes2
Has abstractyes

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