Facilitating women entrepreneurship in Canada: the case of WEKH
Bibliographic record
Abstract
Purpose This paper aims to provide a multi-level framework for exploring women entrepreneurship in Canada. The authors examine the Women Entrepreneurship Knowledge Hub (WEKH), a platform to advance women entrepreneurs from diverse backgrounds. Design/methodology/approach The authors analyze the major elements associated with the processes and strategies in WEKH through a case study approach. Findings The findings presented in this paper clearly show how creating an inclusive innovation ecosystem linking micro-, meso- and macro-level factors has the potential to advance women entrepreneurship Research limitations/implications This case study presented here is in the early phase and results are not yet available. Practical implications The lessons from WEKH provides a model for other countries. Social implications Entrepreneurship drives economic development and gender equality is a critical sustainable development goal. WEKH activities will advance opportunities for women by creating a more inclusive innovation ecosystem. Originality/value WEKH is a knowledge hub in Canada that aims to help foster women entrepreneurship in Canada related to the women entrepreneurship strategy national program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".