Bibliographic record
Abstract
I am an Inuk art therapist who lives and works in Canada. It took me a long time to be able to say that, to get comfortable identifying myself as Inuk, with my blue eyes and blonde hair. My grandmother was Inuk from Labrador. Like many Indigenous people in Canada, she lost her status in the eyes of the Canadian government as soon as she left the reserve. The loss of status meant the loss of treaty rights and benefits. My father fought for over a decade to have his status, and that of his daughters, recognized. Given my appearance and the fact that I grew up outside of my ancestral territory, describing myself as Inuk can open up a wide variety of dialogues, depending upon whom I am talking with and what we are discussing. This is true in my art therapy practice, as well. I am very aware of the privilege that comes with having fair skin and I am cognizant of the fact that I did not grow up with the oppression and systemic racism that affect many, if not all, Indigenous peoples, including members of my family.
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 | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.023 |
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".