Where the past meets the present: an assessment of the social and ecological determinants of well-being among Gimli fishers
Bibliographic record
Abstract
Lake Winnipeg is under examined, yet a very interesting lake. This 10th largest lake in the world supports a small-scale fishery, which today is pre-dominantly for walleye. Currently, the fishery is very strong. Historically, however, it has been characterized by fluctuations in both catches and returns. The end of the 1960s and the beginning of the 1970s was a turning point for the fishing industry. At that time, the fishery experienced declining catches and diminishing returns. In order to reverse the trend, the Provincial Government of Manitoba introduced a quota system while the Federal Government established the Freshwater Fish Marketing Corporation (FFMC) to handle the marketing of freshwater fish. Today, the fishery on Lake Winnipeg has been at a record level of production and these two institutions play a significant role in the fishery. To gain an insight into fishers’ social context in the Gimli area, a social well-being analysis was applied following design of the ESRC Wellbeing in Developing Countries Research Group adapted by Sarah Coulthard and colleagues. The social well-being analytical tool shed light on three domains of fisher’s life; material well-being, subjective well-being and relational well-being. Together, these domains give an insight into how satisfied fishers are with their current social environment. The analysis of fishers’ social experience in the Gimli area shows that current policy seems to be working equally well in the areas of relational and subjective well-being. In fact, the quota system and the FFMC still have great impact on the fishers, not only from an economic view but also for their subjective values and relational well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".