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Record W2344758453 · doi:10.13023/etd.2016.065

"An Everlasting Service"

2016· article· en· W2344758453 on OpenAlexaboutno aff
Mary E. Osborne

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsWorld War IISpanish Civil WarLegislationService (business)Public administrationPolitical scienceLawSociologyHistoryEconomy

Abstract

fetched live from OpenAlex

The public tends to think of war memorials as fixed monuments, but I argue that the American and Canadian Legions served as living memorials that acknowledged veterans’ war-time service by providing service to veterans and to the public. This dissertation focuses on how Legionnaires interacted with one another and with their local communities during the interwar years to construct memories of the First World War. By analyzing local chapter records from Michigan, New York, and Ontario, Canada, this case study highlights the contrast between the organizations’ national and local activities. The local posts’ and branches’ wide range of activities complicated the national organizations’ collective memories of the First World War. A new way to construct a holistic depiction of veterans’ organizations is to study them as living memorials. From this perspective, all of their day-to-day activities fulfill the larger purpose of preserving and perpetuating the memory of their war experiences. At the national level, the American and Canadian Legions advocated for legislation to benefit veterans, but it was primarily at the local level where rank-and-file members shaped the Legions’ collective memories of the war. This study explores elements of those memories, including sacrifice, service, and camaraderie, through the tensions that sometimes arose between the national leadership and the local chapters and compares the American and Canadian Legionnaires’ experiences.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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
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
Published2016
Admission routes1
Has abstractyes

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