Policy Change Towards Equity and Inclusion is Good for Science in Canada
Bibliographic record
Abstract
[para.1.]: "In 2019, the Canadian post-secondary education (PSE) sector, and particularly the research enterprise, saw the implementation of significant initiatives relating to increasing equity, diversity and inclusion (EDI) in research, across all disciplines, including all scientific research supported by the three tri-councils. Overall, research culture in Canada has historically moved toward equity at a glacial pace and is behind other jurisdictions such as the US, UK and Australia in adopting policy-driven approaches to improved EDI in PSE. In 2019, there are now a number of policy changes that include (but are not limited to) the requirement for all Canadian PSE institutions to develop equity plans, increased accountability in the CRC program, expectations of applicants to integrate EDI and SGBA+ analysis in grant applications, and mandatory peer-review training on implicit bias. Canadian institutions can now also voluntarily participate in the recently launched Dimensions: EDI charter, which expected organizations to develop, implement and assess multi-year action plans which address their own institutional policies and programming initiatives towards identifying individual structural and systemic biases that limit full participation of members of the federally designated groups (women, Indigenous peoples, persons with disabilities and members of visible minorities) and other under-represented communities (e.g., LGBTQ2S+).”
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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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.040 | 0.028 |
| Scholarly communication | 0.036 | 0.012 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".