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Record W3028512398 · doi:10.1111/ssqu.12797

Colonial Military Garrisons as Labor‐Market Shocks: Quebec City and Boston, 1760–1775

2020· article· en· W3028512398 on OpenAlexaffabout
Jeremy Land, Vincent Geloso

Bibliographic record

VenueSocial Science Quarterly · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsThe King's University
Fundersnot available
KeywordsPopulationColonialismPoliticsWageShock (circulatory)Demographic economicsEconomic historyEconomicsDemographySociologyPolitical scienceLabour economicsLawMedicine

Abstract

fetched live from OpenAlex

Abstract The military occupation of Boston in 1768 shocked the city's labor market. The soldiers, who were expected to supplement their pay by working for local businesses, constituted an influx equal to 12.5 percent of greater Boston's population. To assess the importance of this shock, we use the case of Quebec City, which experienced the reverse process (i.e., a reduction in the British military presence from close to 18 percent of the region's population to less than 1 percent). We argue that, in Boston, the combination of the large influx of soldiers and a heavy tax on the local population in the form of the billeting system caused an important wage reduction, while the lighter billeting system of Quebec City and the winding down of the garrison pushed wages up. We tie these experiences to political developments in the 1770s.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.054
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes2
Has abstractyes

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