Equity, diversity and inclusion in Canada's forest sector labour force: Are we making progress?
Bibliographic record
Abstract
Around the world, the labour force supporting commercial forestry has been male-dominated and Canada is no exception. Women, Indigenous Peoples and immigrants in Canada often face systemic barriers such as racism, sexism that result in specific inequalities including income disparities, job segregation, and uneven opportunities for training and mentorship. In response, federal and provincial governments, industry, and educational institutions have introduced policies and taken action to enhance equity, diversity, and inclusion (EDI) in the labour force across multiple sectors. In this paper, we explore Canada's progress in building a diverse and equitable forestry labour force. We analysed data from national forestry strategies (1981–2019), State of Canada's Forests Reports (1990–2023), and the quinquennial national Census (1991–2021), using proxies to examine progress in employment opportunities and representation of three equity-denied groups: women, Indigenous Peoples, and immigrants. Although there are high-level policies for EDI, federal government documents for the forest sector revealed little attention to EDI, with the exception of promoting opportunities for Indigenous workers. Census data show slow and uneven progress with respect to labour force participation, income, and job segregation in forestry. While there is progress in opportunities for Indigenous people, the data show that they still have lower incomes and occupy fewer management positions than others employed in commercial forestry. We reflect on several limitationsin the available data and conclude that if the forest sector in Canada and other similar contexts seeks to advance EDI in its forestry labour force, it must commit to broad motivations for diversity beyond industry competitiveness, set clear targets, introduce new practices, take action and publicly report on the results.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".