Infusion rather than isolation: Integrating principles of equity, diversity, inclusion, decolonization, and Indigenization in toolkits for remote instruction during the COVID-19 pandemic
Bibliographic record
Abstract
In the spring of 2020, our Centre for Teaching and Learning (CTL) developed the Transforming Teaching and Teaching Assistant Toolkits, consisting of in-house and curated open-access resources on various aspects of remote teaching, along with accompanying webinars. We deliberately infused principles of equity, diversity, and inclusion (EDI) and decolonization and Indigenization across all aspects of the resources for several reasons: our CTL’s commitment to these principles as institutional priorities that are the responsibility of all staff, numerous theorists’ advocacy to adopt inclusive pedagogies across the curriculum rather than tokenistic “add-and-stir” gestures, and a desire to counter the inequities in education and society at large re-exposed and perpetuated by the COVID-19 pandemic. We share our approach, explore its impact by outlining the toolkits’ design and delivery and by analyzing data from a survey of instructors who engaged with the toolkits, and propose some strategies for educational developers engaged in resource development to undertake their own infusion initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.003 | 0.007 |
| 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".