Bibliographic record
Abstract
Abstract: As demonstrated by coverage of the seasonal cod fisheries in Lofoten, perceptions and imaginaries of fatalities in Norwegian fisheries were changing from the mid-nineteenth to the mid-twentieth century. Eilert Sundt’s efforts to humanize fatalities in the fisheries in the 1850s and 1860s were followed by major technological innovations—telegraph, newspapers, insurance, motorization, and weather forecasting—from the late nineteenth century that made fishing safer while at the same time transforming cultural interpretations of fatalities to emphasize “risk” rather than “danger.” Fatalities were no longer primarily products of threats emanating from the environment; they were primarily about human technologies, institutions, and decisions. Abstract: Comme le montre la recherche sur les pêcheries saisonnières de morue à Lofoten, les perceptions et les imaginaires liés aux décès dans les pêcheries norvégiennes ont évolué entre le milieu du XIXe et le milieu du XXe siècle. Les efforts d’Eilert Sundt pour humaniser les décès dans ces pêcheries entre 1850 et 1860 ont été suivis, à compter de la fin du XIXe siècle, par des innovations technologiques majeures — le télégraphe, les journaux, les assurances, la motorisation des bâteaux et les prévisions météorologiques — qui ont rendu la pêche plus sûre tout en transformant les interprétations culturelles des décès pour mettre davantage l’accent sur le « risque » plutôt que sur le « danger ». Les accidents mortels n’étaient plus essentiellement le résultat de menaces provenant de l’environnement mais étaient principalement imputables aux technologies, aux institutions et aux décisions humaines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".