MétaCan
Menu
Back to cohort
Record W2990942515 · doi:10.3138/chr.2018-0073

The Hidden Face of Consumption: Extending Credit to the Urban Masses in Montreal (1920s–40s)

2019· article· en· W2990942515 on OpenAlexvenueaboutno aff
Sylvie Taschereau, Yvan Rousseau

Bibliographic record

VenueCanadian Historical Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Context (archaeology)CreditorGovernment (linguistics)Consumer spendingFace (sociological concept)Balance (ability)EconomicsAdvertisingBusinessCommerceDebtSociologyFinanceRecessionKeynesian economicsSocial scienceHistory

Abstract

fetched live from OpenAlex

According to many historians and sociologists, mass consumption did not arrive in Canada until the 1950s or even the 1960s. However, this article shows that consumption by low-wage earners in the Canadian metropolis of Montreal was already beginning to transform and expand during the interwar period. As much as consumers, government helped drive these changes by relaxing the legal framework regulating credit practices as well as relations between small debtors and their creditors. The growing use of consumer credit by working-class families was at the heart of these transformations. In this article, the files of depositors placed under the protection of the Lacombe Act provide a unique window on this phenomenon and associated consumer behaviours. An analysis of public debates on the consumer habits of urban workers offers additional insight and context. We advocate for a broad understanding of consumer credit, while rejecting the familiar dichotomy between the consumption of durable goods and the purchase of basic necessities. Necessities and luxuries are constructs that have evolved over time, and the Lacombe files highlight how low-income households combined different sources of credit to incorporate and balance these two types of expenses within their tight budgets.

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)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.234
Teacher spread0.195 · 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
GenreReview

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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Historical ReviewSame topicHousing, Finance, and NeoliberalismFrench-language works237,207