Bibliographic record
Abstract
Historian Rosemary Ommer gave the W. Stewart MacNutt Memorial Lecture at the University of New Brunswick in 1993, a year after the collapse of groundfish stocks had led the federal government to issue fishing moratoria in Atlantic Canada. It was entitled "One Hundred Years of Fishery Crises in Newfoundland." As a historian of the fishery and a resident of St. John’s, Ommer used this occasion to grapple with the meaning of the dire economic crisis now facing Newfoundlanders and to reflect on the historian’s role in the societal search for answers. What can the historian offer, she asked? A first step, Ommer told the audience, was for scholars to understand the crisis and provide insight into what went wrong: "We must trace where it came from, we must ask why it was not foreseen and prevented, and we must seek out the most useful approach to a solution for the future. In so doing, we will need to be aware of the implicit ideologies, beliefs and pressures that underlie the thinking of the policy makers of the day." A second step was to ensure that the "historical record" that was already being marshalled in support of one political position or another was not distorted beyond recognition. A steady stream of studies has since appeared on the crisis in the Atlantic fisheries.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.435 | 0.207 |
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".