Bibliographic record
Abstract
There is a paucity of feminist and trauma content, analysis, and curriculum in Canadian schools of social work. Accordingly, Canadian social work students graduate with limited education on the dynamics underpinning oppression and systemic inequality, root causes of violence/trauma, or feminist analyses of these issues. The author designed an innovative feminist- informed trauma social work course which featured a feminist integrated trauma (FIT) model that she and Wilkin developed. Hillock covers three course design themes: an examination of her social location/identity in terms of acknowledging her privilege and bias; a presentation of core learning objectives underpinning the course; and the identification of the feminist, structural, and Indigenous approaches, methods, and activities that inform the teaching of this course. Additionally, she details the course&s;s content, readings, and weekly topics, major theoretical viewpoints (including the FIT model), and important practical application pieces. She summarizes her experiences including fears, challenges, teaching/learning opportunities, successes, trigger warnings, and student and colleague reaction/backlash. Finally, Hillock discusses the importance of this work to social work education and suggests implications for future growth and evolution.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.017 |
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".