Untapped potential? Attitudes and behaviours of forestry employers toward the Indigenous workforce in Quebec, Canada
Bibliographic record
Abstract
The skilled labour shortage in the natural resource sector is a major issue in North America, particularly in the Canadian forestry sector. In the province of Quebec alone, 15 000 positions will need to be filled by 2022. At the same time, many Indigenous communities are seeking to develop employment opportunities, as they have high unemployment rates and a young and growing population. But are forestry employers creating an environment conducive to the recruitment, integration, and retention of an Indigenous workforce? We interviewed 22 directors and human resource managers from 19 forestry businesses (16 non-Indigenous and 3 Indigenous) in Quebec, with a view to answering this question. Employer narratives suggest that they have only just begun to see the potential of the Indigenous workforce and put in place diversity management practices. Partnerships between Indigenous communities and forestry businesses, development of alternative training and skill development methods, and awareness-raising among employees and employers were found to favour recruitment, integration, and retention of Indigenous workers. Conversely, according to participants, stereotypes, discrimination, lack of inclusion measures, drug and alcohol use, and lack of training reduce the potential for Indigenous people to join the forestry workforce.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".