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Record W4313402902 · doi:10.1017/eso.2022.48

Writing and Reading New Markets: Insurance in Quebec, 1931–1960

2022· article· en· W4313402902 on OpenAlexaboutno aff
Heather Nelson

Bibliographic record

VenueEnterprise & Society · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureInsurance lawBusinessWork (physics)PurchasingKey person insuranceGeneral insuranceInsurance policyFinanceLawMarketingPolitical science

Abstract

fetched live from OpenAlex

The Wawanesa Mutual Insurance Company, successful in western Canada, struggled to replicate its business model in Quebec in the 1930s. The absence of financial responsibility law in Quebec, which made purchasing automobile insurance nearly compulsory for drivers, created a unique opportunity. Wawanesa could insure taxis and fleets in a market where uninsured drivers were the norm. To accommodate this change, it became a direct writer in Quebec. The company also loosened its previously rigid management style to allow branch managers to make regionally appropriate decisions. Insurance companies that fled Quebec in the 1940s would struggle to compete upon their return, because Wawanesa became a market leader. The introduction of financial responsibility law in the province in 1961 would grow the company in the years that followed. As historians work to understand the importance of regional and legislative change to the insurance industry, this story provides a snapshot of a single company in a single market.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0140.007
Scholarly communication0.0060.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.205
Teacher spread0.193 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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