Bibliographic record
Abstract
A Note on TerminologyThe names used to refer to the Aboriginal peoples of what is now Canada have undergone changes and shifts throughout history.In 1929, the time of the Franklin Motor Expedition that is the focus of this book, "Indian" was the most commonly used descriptive term for indigenous people.It is still used in legislation, such as the Indian Act.Many people now prefer the collective term "First Nations," which includes status and non-status persons and is currently applied to federally recognized bands.Given that the narrative of this book moves between past and present, I have chosen to use "First Nations" throughout, unless I am quoting.At times I use the more inclusive "Aboriginal," which encompasses all three groups of original peoples and their descendants recognized in the Canadian constitution: Indians (First Nations), Métis, and Inuit.I also use the nations' own names for themselves, wherever possible, though they may differ from those with which the expedition team was familiar.For example, Bungay, Saulteaux, and Plains or western Ojibwe were all used historically to refer to Anishinaabe peoples who moved to the Plains region of Western Canada.The autonym "Anishinaabe" is now relatively common in spoken and written English, whereas the equivalent Cree and Blackfoot autonyms, nehiyaw and Niitsitapi, are used less frequently.It is for this reason that I have chosen to use the collective names "Cree" and "Blackfoot" in this book, rather than "nehiyaw" and "Niitsitapi."Naming is a political act, and I appreciate that my decisions on this matter raise problems of historical accuracy, to which some readers may object.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.037 |
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".