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Record W2969930442 · doi:10.1093/njaf/21.4.200

Who Will Log in Maine's North Woods? A Cross-Cultural Study of Occupational Choice and Prestige

2004· article· en· W2969930442 on OpenAlexaffabout
Andrew Egan, Deryth Taggart

Bibliographic record

VenueNorthern Journal of Applied Forestry · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLoggingPrestigeWork (physics)GeographyOccupational prestigeSocioeconomicsDemographyForestryDemographic economicsSociologyEngineeringPopulationEconomics

Abstract

fetched live from OpenAlex

Abstract Two distinct populations of loggers work in Maine's border counties with Quebec: Maine resident and Quebec resident woodsworkers. This study compared the sense of occupational choice and prestige held by these workers, as well as their sociodemographic attributes. Significant differences in age, education, and logging experience were found between these two populations. In addition, Maine resident loggers appeared to exhibit less resignation to woods work than their Quebec counterparts. However, Quebec resident loggers indicated that their profession was held in higher esteem among the public than did loggers from Maine. Over two-thirds of respondents from both populations would not encourage a son/daughter to be a logger, despite considerable familial attachment to logging. Results may have implications for logging labor supply, labor recruitment efforts, and logging mechanization in a region heavily dependent on the forest products industry. North. J. Appl. For. 21(4):200–208.

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)0.002
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 score0.691
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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

Citations25
Published2004
Admission routes2
Has abstractyes

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