Bibliographic record
Abstract
In the period between March and July 2010, I was able to conduct a study trip to the east and west coast of Canada with the kind financial support from the Centre for Sami Studies at the University of Tromsø as part of my PhD program. Without their support, the travel would not have been possible and it has contributed to expanding knowledge and creating contacts in a growing field of study. Many people helped to make this trip come about as successfully as it did. Thank you to my supervisor Svein Jentoft and to Else Grete Broderstad and Stine Barlindhaug and others who kindly provided contacts in Canada. Most of all, I am grateful to Barbara Neis and Peter Armitage who hosted me in St. John’s for almost two months, and also Tony Davis and his family who took me in for two weeks in Nova Scotia. The goal of the trip was to learn more about methodologies1 and methods for documenting fisheries in indigenous and small coastal communities and applying these to the coastal Sami context and my own research on coastal Sami fisheries. I was interested in both fisheries research methods in general and methods for documenting indigenous land use and occupancy, in addition to how the different research institutions and projects in Canada address indigenous fisheries issues. This report contains the background for the research trip, an overview of travels and activities during the stay, and a more detailed report from two of the places visited during the stay, focusing on Mi’kmaq fisheries in Atlantic Canada and salmon farming issues in British Columbia. The most central people and institutions have provided feedback to the report before it was submitted to the board at the Centre for Sami Studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".