Bibliographic record
Abstract
This research critically examines how Canadian government policies position women entrepreneurs through a discourse analysis grounded in poststructuralist feminist theory. Using publicly available texts from the Women Entrepreneurship Strategy (WES) Ecosystem Fund and the website-based content of the 53 funded organizations as data sources, this study deconstructs the language shaping women entrepreneurship policy. The findings reveal three dominant discourses: positioning women as untapped economic resources, framing women as deficient in entrepreneurial skills requiring training, and perpetuating neoliberal feminist perspectives that align entrepreneurship with economic growth rather than gender equity. These discourses reinforce traditional gender roles and power dynamics, marginalizing women entrepreneurs and overlooking structural barriers within the entrepreneurial ecosystem. This research highlights the limitations of deficit-based policy interventions, arguing for systemic reform that prioritizes equity and addresses structural inequities. By situating Canada’s policy approach within a broader international context, the study provides critical insights for policy makers in other countries that may view Canada as a model for women entrepreneurship policy. The findings underscore the urgency of moving beyond training-focused interventions to reimagine entrepreneurship ecosystems in ways that challenge male norms and foster inclusivity. This study also identifies opportunities for future research to evaluate the long-term impacts of Canada’s WES, particularly as it concludes in 2024, offering valuable lessons for global policy development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.043 | 0.022 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".