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Record W4253825518 · doi:10.24124/2014/bpgub977

Femininity and female identity of forest industry workers in Northern British Columbia from 1960 to 2000.

2014· dissertation· en· W4253825518 on OpenAlexaboutno aff
Kathryn S. Doucette

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFemininityRealmForest industryIdentity (music)Gender studiesWork (physics)SociologyPolitical scienceGeographyForestryEngineeringAestheticsArt

Abstract

fetched live from OpenAlex

This research asked what effects, if any, working in the male-dominated forest industry had on femininity and female identity of women who choose to work in the forest industry of Northern British Columbia. In the literature when women are mentioned attention is focused on their relationship to men who are involved within forest industries. By using Feminist Standpoint Theory the participants' stories are added to the discourse of the forest industry. What the findings propose is that the participants experienced an increase in strength - physical, mental and emotional - but they did not feel that their employment had any lasting effect on their femininity and female identity. While the women had many unique experiences interacting with forest industry employment, the same employment patterns are observable while studying male employees in the forest industry, such as the process to entry into the industry and the necessity of proving their capability performing the work. Therefore, aspects of women's employment within the forest industry are much like men's but work in the bush' is still socially constructed as a masculine realm. --Leaf 2.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.237
Teacher spread0.228 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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