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Record W2895650968 · doi:10.1017/ssh.2020.16

Military Technology and Sample Selection Bias

2020· preprint· en· W2895650968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Science History · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSelection biasEconomicsBureaucracyRobustness (evolution)Business cycleSample (material)Supply and demandLabour supplyLabour economicsDemographic economicsPolitical scienceMacroeconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Military enlistment is highly selective for reasons of both labor demand and supply. An early-twentieth-century evolution of military technology that shifted the demand for workers of different stature illustrates the importance of labor demand beyond the commonly discussed influences originating with labor supply. English-born soldiers in the Anglo-Boer War (1899–1902) were taller, on average, than those of World War I (1914–18), yet these differences cannot be attributed to standard of living or business cycle influences on the labor market. Rather, we argue, the mechanization and bureaucratization of warfare increased the relative value of shorter people permitting a decline in the average height of soldiers. Technological change over the period of these two wars affected labor demand in a way that must be recognized before using this evidence to test hypotheses about changes in population health.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.112
GPT teacher head0.261
Teacher spread0.149 · 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