Gender analysis of policy-making in construction and transportation: Denial and disruption in the Canadian green economy
Bibliographic record
Abstract
In this chapter we focus on two sectors (construction and transportation) that are deemed critical to Canada’s green economy, but in which women are severely underrepresented. We document and evaluate existing government policies and programs as well as corporate and civil society initiatives from a gender equality perspective. The assessment of gender equality and other forms of diversity in these sectors is complicated by the dearth of gender-disaggregated employment data. Our findings suggest that women are most marginalised in the trades segments of these sectors, in technical positions that require science, technology, engineering and math (STEM) training, and in management, senior leadership and boards of directors of companies. We found that in Canada most green initiatives in these two sectors have been driven by the private sector, non-governmental organisations, municipalities and provincial governments. The federal government has not played an active role in framing and implementing effective policies to enable the transition to a green economy. To optimise the efforts of other actors in the green economy, the federal government must play a stronger leadership role in implementing employment equity policies. Additional research aimed at understanding the outcomes, sustainability and replicability of existing green initiatives is a prerequisite for future legislative changes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".