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Record W4402240069 · doi:10.62320/jfbr.v3i2.51

Do exporters of Canadian forest products price to market?

2024· article· en· W4402240069 on OpenAlexaffabout
Kurt Niquidet, Kyle Sia-Chan, Jonathan Kan, Lili Sun, Craig Johnston

Bibliographic record

VenueJournal of Forest Business Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsBank of CanadaCanadian Sport Centre PacificNatural Resources CanadaBC Innovation CouncilUniversity of British Columbia
Fundersnot available
KeywordsBusinessCommerceInternational trade

Abstract

fetched live from OpenAlex

Forest products in Canada contribute significantly to the Canadian trade balance. Canadian producers depend heavily on export markets raising the question: how do exchange rate fluctuations impact Canada’s competitiveness in foreign markets? The paper applies a fixed-effects model with individual slopes to investigate this question. Twenty years of monthly data are employed to study the pricing-to-market (PTM) behaviour of Canadian softwood log, lumber and pulp exports as the exchange rate changes. We find a great degree of incomplete exchange rate pass-through, with PTM being apparent for Canadian exporters, particularly in major markets. The export price adjustment tends to mitigate the effect of exchange rate fluctuations on foreign currency prices of Canadian products in most cases. This pricing behaviour reflects exporters’ desire to stabilize their share of the destination market.

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)0.000
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 score0.040
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.047
GPT teacher head0.320
Teacher spread0.273 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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