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Record W7010025423

Gender, retirement & mobility: a case study of the Lobster Enterprise Retirement Program in Newfoundland

2019· dissertation· en· W7010025423 on OpenAlexaboutno aff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantageWork (physics)Power (physics)InequalityPaid workAction (physics)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.287
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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