Equity, diversity and inclusion in Canada's forest sector labour force: Are we making progress?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.026 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it