Bibliographic record
Abstract
In If This Is Your Land, Where Are Your Stories, Ted Chamberlin wants to persuade non-Aboriginal Canadians to acknowledge Aboriginal title to the land. This is a new idea and a radical project. Ted reassures the fearful that title to the land is a fiction and would not change anything, but, of course, it would also change everything, because it would change how nonAboriginal Canadians think of Aboriginals and of themselves. We (EuroCanadians, Ted’s ‘We’) would need a new story, one that took in their story. But to have a new story or to receive another’s story, people must first realize their own need for stories. There is something counter-intuitive here, even scandalously so. The premise of most current criticism is that only white English-Canadians have been allowed to tell their story; their story has been propagated as the only story; and those on the margins with different faces and speaking in different languages or with different accents have had their stories silenced or, in the case of Aboriginals, taken away from them. As Mongane Wally Serote puts it, in a different context:
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".