Equity in Education: Examining the HPCDSB Resource Hub a Mechanism for Teacher Professional Learning about Equity
Bibliographic record
Abstract
This project examines the development of the Equity, Diversity, Inclusivity, and Anti-Racism (EDIAR) Resource Hub at the Huron-Perth Catholic District School Board (HPCDSB) of Ontario. The Resource Hub is an online platform which is designed to assist educators and school leaders by providing access to a comprehensive depository of resources which includes articles, policy documents, and training modules. Its primary goal is to promote equity in education and facilitate professional development in alignment with Ontario’s Equity and Inclusive Education Strategy (2009) and the Equity Action Plan (2017). The project investigates how the Resource Hub aligns with these strategic frameworks and analyzes its effectiveness in transforming classroom practices, school environments, and leadership approaches within the HPCDSB. By analyzing the development of the Resource Hub, the study explores its contribution to advancing inclusivity and social justice within the educational setting. The project highlights how the Hub supports the development of equitable teaching practices and fosters a culture of inclusivity among staff and students. The project underscores the Resource Hub’s significance in enhancing the accessibility of resources for educators and approaches effective recommendations for refining its functionality and broadening its access. These insights aim to reinforce the Resource Hub’s scope to support educators in their professional growth and drive systemic changes towards greater equity, diversity, and inclusivity in education.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".