Ricerca del profitto e protezione dell’ambiente. Un binomio possibile? Il settore della pesca in Quebec
Bibliographic record
Abstract
In public opinion, fishing as an industry has very often been associated with various environmental issues. In this article, we focus on the environ mental discourse of fishers, as well as their behaviour and professional practices. Our reflection is based on a qualitative research project, which took place between 2016 and 2018 in the Gaspésie region of Québec, Canada. The professional «ethos» of fishers is strongly linked to nature and the environment. Their proximity to nature makes them witness to many environmental issues, thus developing an environmental awareness. The captain-owner’s discourse on the environment revolves mainly around their vulnerability to changes in the ecosystem. Our analysis focused on two main categories of environmental behaviours: voluntary and imposed actions. Voluntary environmental actions are mainly motivated by the desire to protect the marine resource in order to ensure the sustainability of fishing sector. Environmental behaviour imposed by fisheries regulatory bodies mainly concerns fishing quotas, fisheries management zones, fishing gear and bycatches. The degree of acceptability of these imposed actions may greatly vary.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".