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Record W7067681992

Nature's Past Episode 035: Histories of Canadian Environmental Issues, Part V – Fisheries, Regulation, and Science

2013· other· en· W7067681992 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2013
Typeother
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Context (archaeology)State (computer science)GlobeFisheries managementHydroelectricity
DOInot available

Abstract

fetched live from OpenAlex

The need for thoughtful histories on contemporary Canadian environmental issues has never been more critical than it is regarding the present state of the country’s fisheries. In June 2012, funding for fisheries-related research and protection was significantly curtailed as part of federal government cuts and amendments to the Fisheries Act included in the C-38 omnibus budget bill. These changes, however, are not unprecedented. By placing Canada’s fisheries and marine environments in greater jeopardy than they’ve ever been, the changes fit into a longer pattern of government undermining of the law that go back as far as the 1970s. In response, dozens of environmentalists, researchers and scientists have criticized the cuts as misinformed and dangerous. In a letter to the Globe and Mail soon after bill C-38 was announced, four former Fisheries and Oceans ministers wrote they believe these changes “will inevitably reduce and weaken the habitat-protection provisions” of the Fisheries Act. \n \nCanada’s fisheries have been subjects of controversy and sites of tension for over 200 years. On the east coast, small-scale, inshore fisheries (the norm since the seventeenth century) gave way to large-scale, scientifically-managed commercial fisheries. Technological advances, globalizing market structures, and an ever-increasing reliance on experts, created a context in which the Department of Fisheries and Oceans shifted the purpose of fisheries from meeting human needs to meeting maximum sustainable yields and total allowable catches. The result was the collapse of the North Atlantic cod fishery in the early 1990s. On the west coast, the defence of the salmon fishery against hydroelectric development on the Fraser River in the middle of the nineteenth century is one bright spot in a story of over-fishing, habitat loss, and the negative side-effects of commercial-scale aquaculture. The artificial state border between Canada and the United States in the Salish Sea, which did not reflect the migratory lives of pacific salmon, created the conditions for unmanageable fish banditry. Inland, freshwater fisheries have experienced similar stories of over-harvesting, threats to fish habitat, and denial of Native resource rights. Around the Great Lakes, First Nations experienced competition from non-native commercial fishermen as early as the 1830s, spent much of the late nineteenth century resisting efforts by the Ontario government to eliminate their traditional rights, and fought a series of legal battles during the twentieth century to regain autonomy over their fisheries. \n \nWhile certain species have begun to recover in the Great Lakes, several species found in Canada’s coastal waters have not. According to the Fisheries and Agriculture Organization of the United Nations, roughly 75% of global fish stocks are fully exploited or have collapsed. Canada has played a leading role in bringing us to the brink of global fisheries collapse. Given this scenario, insights from scholars writing on the history of fisheries in Canada is critical if further catastrophe is to be avoided. \n \nOn this episode, we speak with five leading historians of Canadian fisheries, including Dean Bavington, Stephen Bocking, Douglas Harris, Will Knight, and Liza Piper. \n \nPlease be sure to take a moment to fill out a short listener survey here.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.960
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.008
Science and technology studies0.0400.012
Scholarly communication0.0120.003
Open science0.0020.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.020
GPT teacher head0.188
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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