MétaCan
Menu
Back to cohort
Record W3127250239 · doi:10.3138/chr-2020-0006

“Almost Too Late”: The Merchant Navy Redress Campaign and the Struggle for Veteranhood

2021· article· en· W3127250239 on OpenAlexvenueaboutno aff
Tim Cook, Matthew J. Moore

Bibliographic record

VenueCanadian Historical Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsRedressNavyGovernment (linguistics)Political scienceState (computer science)LegislationPublic administrationWorld War IILaw

Abstract

fetched live from OpenAlex

This article will assess the role of Canada’s Merchant Navy during the Second World War and appraise shifting government attitudes towards the civilian mariners in veterans’ legislation in the postwar period. By examining the factors that contributed to the civilian mariners’ initial exclusion as veterans, this study sheds light on the complex process whereby the state evaluates and then reassesses what is owed to those who serve. The redress campaign to achieve veteranhood from the early 1980s provides new insight into how these veterans marshalled resources and mobilized their own personal wartime histories as part of a broader movement in that decade of re-engaging with the Second World War. As part of their efforts, the mariners first had to engage with other veterans and win them over as allies. In their struggle with the state, the mariners asserted that the risk they faced, the casualties they suffered, and the wartime government’s management of the fleet, meant that Ottawa had greater responsibilities in regard to their postwar care and that they had earned veteran status. This is the first sustained study of the redress campaign that ran for almost twenty years, from the early 1980s to 2000. It demonstrates that the concept of “veteranhood” is fluid, and that once neglected wartime narratives can be reincorporated into the nation’s military legacy.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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: Review · Consensus signal: Review
Teacher disagreement score0.322
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.264
Teacher spread0.232 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Historical ReviewSame topicCanadian Identity and HistoryFrench-language works237,207