Bibliographic record
Abstract
Extract The names assigned to both places and people over time can tell us a great deal both about the history of colonization and about the perspectives and attitudes of differing groups of people at any given time. Left unexamined, certain names have the power to distort our understanding of the past. As historians of empire and colonization note, the renaming of Indigenous spaces lay at the very heart of the colonial endeavor and the erasure of the continuing presence of Indigenous people within a colonial framework.1Close Always a challenge, particularly in the context of a story as complex and multifaceted as that told here, is determining what name is best utilized for a given place or people at a particular time. The island group that lies off the coast of what is now British Columbia long known to the Haida people as Haida Gwaii (Xaayda Gwayy′), for example, was renamed Queen Charlotte’s Islands by British explorers and referred to as the Queen Charlotte Islands by British colonial authorities for over two centuries, even as the Japanese Canadian fishers active along the coast prior to World War II knew them as Kuichi Airan.2Close Generally, I have chosen to use the name for a given place that is most revealing of its time and that gives us the greatest insight into the attitudes or perspectives of the actors under discussion, while always remaining cognizant that the vast majority of this borderlands region remained Indigenous space, even as it was incorporated within the boundaries of what are now Alaska, British Columbia, and the Yukon Territory.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.208 | 0.214 |
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".