Bibliographic record
Abstract
The health of Indigenous girls in Canada is often framed and addressed through health programs and interventions that are based on Western values systems that serve to further colonize girls’ health and their bodies. One of the risks of the recent attention paid to Indigenous girls’ health needs broadly and to trauma more specifically, is the danger of contributing to the “shock and awe” campaign against Indigenous girls who have experienced violence, and of creating further stigma and marginalization for girls. A focus on trauma as an individual health problem prevents and obscures a more critical, historically-situated focus on social problems under a (neo)colonial state that contribute to violence. There is a need for programs that provide safer spaces for girls that address their intersecting and emergent health needs and do not further the discourse and construction of Indigenous girls as at-risk. The author will present her work with Indigenous girls in an Indigenous girls group that resists medical and individual definitions of trauma, and instead utilizes an Indigenous intersectional framework that assists girls in understanding and locating their coping as responses to larger structural and systemic forces including racism, poverty, sexism, colonialism and a culture of violence enacted through state policy and practices.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.056 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".