Bibliographic record
Abstract
Adolescent at the time of my classical studies, in my room on the desk where I did homework, I had a red hardcover volume that I was so proud of -like a medieval monk beholding a rare manuscript.It was The Indians of Canada by Diamond Jenness.I consulted it incessantly, to relearn each and every day some rare and precious knowledge I was afraid of forgetting.I would find out much later what Innu writer An Antane Kapesh meant when she asserted that she was proud to be a "savagesse": a term considered by some to be cursed, yet she translated it literally as the joy of living on savage lands.Much in the same way I naively believed "Indian" was among the most beautiful word in the world.I thought it nice to be an Indian.Yet history got the better of me; it got the better of us all.These "savages" and "Indians," they disappeared, cast out with other dirty, decried words.So, we changed the words, thinking it would change the world, that by no longer saying this word or that, the problem would be resolved.We all know that someone who is visually disabled is not entirely blind, just as a person with reduced mobility is not wholly disabled; it would appear that someone who is Native is much less Indian.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.313 | 0.170 |
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".