Participation of Indigenous employees in the Quebec's forestry sector: opportunities and barriers
Bibliographic record
Abstract
Purpose The purpose of this study is to better understand the factors influencing the attraction of Indigenous workers to the Quebec forestry sector. Design/methodology/approach Using a collaborative approach, 64 semi-structured interviews were conducted between 2016 and 2018 with workers and stakeholders from three Indigenous communities in Quebec, Canada. Findings The results highlight the motivations for choosing a job in the forestry sector, including family and friends, attachment to the territory, financial necessity, the search for a challenge and a sense of pride. They also show some of the obstacles to holding a job in forestry, namely work–life conflict, transportation, job insecurity, education and personal problems. Social implications Indigenous people have a lower employment rate than non-Indigenous people, which can be explained by a number of factors that hinder their integration into the labour market. They nevertheless represent an interesting labour pool for companies working in the natural resources sector. This study sheds light on the opportunities and barriers to attract this workforce. Originality/value The study is one of the few to use theoretical frameworks focused on motivation and a qualitative approach to data collection in order to examine to examine the attraction of Indigenous workers to the forestry sector in Quebec (Canada) from a worker's perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".