Bibliographic record
Abstract
Despite reams of anti-oppressive rhetoric and the fact that social work is a female-majority profession, feminist content, analyses, and curriculum are not yet centred in any significant way in Canadian schools of social work. As a result, students may graduate with limited exposure to, and superficial understandings of, the dynamics underpinning gender-based oppression and systemic inequality, root causes of violence/trauma, or feminist analyses of these issues. The same can be said in terms of social work education content and material related to climate change/justice, environmentalism, and sustainability, which leaves social work practitioners unprepared to handle current and upcoming climate disasters/crises and with inadequate knowledge and skills to help populations who are most vulnerable to their impacts. To fill these gaps, the author presents a new educational approach that she developed called eco-femagogy. This critical red-green perspective encourages interdisciplinary teaching/learning environments; centres Indigenous, eco-feminist, intersectional/critical race, and socialist/Marxist knowledges and analyses; mobilizes colleagues, students, and citizens towards social and environmental change; and supports social and climate justice/action. Recommendations for improving social work education are detailed. Using concrete examples, she details how this innovative approach offers a progressive post-covid framework to transform our teaching, curricula, and methods.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".