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Record W1483107805 · doi:10.24908/ss.v12i4.4764

Making Up Soldiers: The Role of Statistical Oversight and Reactive Path Dependence in the Effectiveness of Canada’s WWII Mobilization Program 1940-1943

2014· article· en· W1483107805 on OpenAlexafffundabout
Scott Thompson

Bibliographic record

VenueSurveillance & Society · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMobilizationLegislationWorld War IIGovernment (linguistics)Political sciencePublic administrationState (computer science)Social mobilizationPath (computing)Political economyEconomic growthLawSociologyPoliticsEconomicsComputer science

Abstract

fetched live from OpenAlex

This paper demonstrates the relationship between specific surveillance technologies and state actors’ capacity to assert governmentally ascribed social categories, and their associated performances, onto individuals and populations. During the Second World War in Canada, the government passed legislation to conscript individuals into the Armed Forces. The program commenced in 1940, mandating a registration of all those over the age of sixteen. The conscription of men classified to be “mobilized” began soon after, however, the effectiveness of the National Registration and mobilization system to conscript these identified individuals increased dramatically with the adoption of a new set of surveillance technologies in March 1942. These changes led to significant increases in the number of men that were conscripted per month after 1942, pointing to the capacity of these new policies and technologies to assert greater statistical oversight over system staff and its targeted populations. These surveillant practices and technologies also prompted the development of a form of reactive path dependence. Together, these two points served as key factors which worked to alter the observable performances of individuals classified for conscription.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.251
Teacher spread0.243 · 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

Citations4
Published2014
Admission routes3
Has abstractyes

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