Energy, Equity & Natural Thresholds in Fisheries In Panel: Toward a Social History of Fossil Fuels
Bibliographic record
Abstract
American Society for Environmental History 2009 Canadian Presentations NiCHE has archived 19 audio presentations from this event Some industrious members of NiCHE (Jim Clifford, Krista Weger, Jay Young, Jennifer Bonnell) recorded some of the sessions and roundtables at the American Society for Environmental History conference in Tallahassee, Fl. (Feb. 2009). Cannadians' presentations and some roundtables of general interest to environmental historians are represented here . Citation : Bavington, Dean. "Energy, Equity & Natural Thresholds in Fisheries In Panel: Toward a Social History of Fossil Fuels." American Society for Environmental History. 26 Feb. 2009. Bio : Canada Research Chair in Environmental History, Assistant Professor of History and Geography, Nipissing University, North Bay, Ontario, Canada and adjunct Assistant Professor at the School of Natural Resources and Environment (SNRE), University of Michigan, Ann Arbor. His current book project Managing Fish, Managing Fishermen offers a synoptic overview of commercial cod fisheries in Newfoundland and Labrador, Canada from the mid-19th century to after the moratorium on cod fishing imposed in 1992. He argues for understanding the cod fishery as a scientifically managed object and exposes the mutually provoked changes between managerial methods on the one hand, and cod, fishermen and fishing techniques on the other. Abstract : In his 1973 essay entitled, Energy and Equity, medieval historian and social critic Ivan Illich observed that the first step toward addressing the energy crisis is to recognize that there are thresholds "beyond which technical processes begin to dictate social relations. Calories are both biologically and socially healthy only as long as they stay within the narrow range that separates enough from too much." In order to uncover what "enough" might mean in the post-collapse cod fisheries of Newfoundland and Labrador, I focus on debates that emerged during the 1850s surrounding the appropriateness of various fishing methods. I interpret the introduction of the cod jigger in the 19th century as marking the transgression of a cultural threshold that allowed the determination of appropriate relationships between codfish and people.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.331 | 0.002 |
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; both teacher heads agree on what is shown here.
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".