Gender, retirement & mobility: a case study of the Lobster Enterprise Retirement Program in Newfoundland
Bibliographic record
Abstract
This thesis explores issues of retirement, restructuring, gender and mobility through an \nanalysis of the Lobster Enterprise Retirement Program (LERP) as it impacted lobster harvesters \non the South Coast of Newfoundland (LFA 11). Employing the tools of Institutional \nEthnography (Smith, 2005), this analysis begins in the work and daily lives of harvesters who \nretired through the LERP and explores the institutional networks and chains of action which \ntransform their lived experience into institutionally manageable outcomes. I conclude, based on \ninterview data from harvesters and key informants as well an analysis of program documents, \nthat the LERP perpetuates historical advantage and disadvantage within the fishery. I explore \nthe specific mechanisms of the program which simultaneously acknowledge and then make \ninvisible the work of women and crew, in effect precluding their access to benefits of the \nprogram. I explore the implications of this structured inequality in terms of unpaid labour, \nnegotiations of a retirement decision within couples, life in retirement, and the ability to find \nland-based work in rural Newfoundland subsequent to leaving the fishery. This project is \nsupervised by Dr. Nicole Power and Dr. Charles Mather and is funded by the On The Move \nPartnership.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".