We have always been here, we’ve been here before: Responding to ongoing anti-trans fascism and colonisation with history, storytelling, and connection to land and community
Bibliographic record
Abstract
This video contribution highlights the importance of active response and continued resistance – not reaction – to the rise in transmisogyny, anti-trans and racist hate. Lorraine urges all caring adults, helpers and professionals to recognise how colonial, capitalist and white supremacist systems fuel a sense of overwhelm and can create embodied distress, especially for those under attack by these systems. LG invites us to be accountable and alert to how these forces often replicate themselves and can find their way into our practices within health care and therapy, especially when it comes to transgender and Two-Spirit health care. Lorraine introduces a potential tool and resource, the We Are Allies project, which is a Health Canada–funded initiative that uplifts Two-Spirit, trans and gender-diverse knowledges, teaches about mis- and disinformation, and (re)connects parents, new learners, potential allies and others with liberatory gender histories and information. Through storytelling and history, Lorraine calls for discernment, connection and local practices of care, rest and solidarity in resisting transphobia and fascism.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".