Study of the Underrepresentation of Women and Women-Identifying IP- Rights Holders, Company Founders and Senior Leadership: Final Report to Innovation Asset Collective
Bibliographic record
Abstract
In 2018 the Government of Canada (Industry, Science and Economic Development Canada) launched its National IP Strategy with a view to helping “Canadian businesses, creators, entrepreneurs and innovators understand, protect and access intellectual property (IP)” 1 Among its many policy initiatives, it identified the underrepresentation of women and womenidentifying2 and Indigenous entrepreneurs in the IP system as areas of concern.3 Encouraging greater success for these and other excluded groups necessarily means facilitating greater participation in generating, protecting and strategically leveraging their IP. In 2020, the Innovation Asset Collective (IAC), which was established pursuant to the National IP Strategy, issued a Call for Proposals to launch a study of women and IP in the data-driven clean tech (DDCT) sector. In 2021, the University of Windsor was selected to conduct this study (the “IAC Study”).\nThe mandate of this study is to engage in a consultation process to better understand women’s experiences in navigating the IP system and, pursuant to the findings of the consultation, to develop and implement specialized education and support initiatives for IAC members in the data-driven clean tech (DDCT) sector.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".