Bibliographic record
Abstract
I first sojourned in northern Manitoba in the summer of 1975 and since then I have expended much energy trying to figure out the relationship between past and present in that region.Although my studies have not attempted to directly connect present conditions to historical knowledge, I feel that some gaps have been filled.Prior to experiencing northern Manitoba, John Ryan, from the Department of Geography at the University of Winnipeg, encouraged an interest in economic geography.Russ Rothney's study on the fur trade provided me with an entry into this particular mercantile economy, which hitherto lacked the appeal that other parts of the world held for me. 1 Subsequently, Arthur J. Ray's Indians in the Fur Trade showed that the problems of mercantile underdevelopment, that is the fur trade, could be studied from the perspective of historical geography.2 In some respects, this present study indicates a culmination of my graduate research on northern Manitoba.My M.A. thesis, 'Manitoba Commercial Fisheries: A Study in Development,' generated no real intellectual interest at McGill University; nonetheless, it affirmed to me the usefulness of a historical geographical approach to a staple industry and indicated the important presence that Natives held in economic life.It was also clear that a focus on the economic history of Native people was both viable and needed.Although distant from northern Manitoba, I found an environment very conducive for historical geography at York University.Moreover, the encouragement I received there to test concepts empirically, rather than pursuing theory through a series of a priori assertions, was welcomed.Skip Ray, John Warkentin, Don Freeman, Conrad Heidenreich, and Hartwell Bowsfield directed course work or supervised my research.My experience at York was entirely positive.The completion of my Ph.D. dissertation at York did not end my research on northern
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.158 |
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".