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The Informal Economy in Non-Metropolitan Canada*

2008· article· en· W2109105747 on OpenAlexaffabout
Bill Reimer

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsMetropolitan areaInformal sectorEconomic geographyEconomicsGeographyEconomic growthArchaeology

Abstract

fetched live from OpenAlex

L'auteur de cet article examine la relation existant entre les économies formelle et informelle, avec des apergus provenant de la recherche sur le Canada rural. L'économie informelle comprend la production, la distribution et la consommation de biens et services ayant une valeur économique, mais qui ne sont ni protégés par un code de loi formel ni enregistrés par des organismes de réglementation endossés par le gouvernement. Plusieurs allégations concernant l'interdépendance des deux économies sont formulées et testées en utilisant les données des Enquêtes sociales générates de 1992 et de 1998 sur l'emploi du temps. Les résultats confirment l'importance de l'economie informelle en tant que filet protecteur, tampon des changements structurels, constructeur de capacités et soutien de l'inclusion sociale. This paper discusses the relationship between the formal and the informal economies with insights derived from research on rural Canada. The informal economy is considered to be the production, distribution and consumption of goods and services that have economic value, but are neither protected by a formal code of law nor recorded for use by government-backed regulatory agencies. Several claims regarding the interdependence of both economies are developed and tested with time budget data from the 1992 and 1998 General Social Surveys. Findings support the importance of the informal economy as a safety net, buffer for structural changes, capacity builder, and support for social inclusion.

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)0.001
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 score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.226
Teacher spread0.180 · 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

Citations23
Published2008
Admission routes2
Has abstractyes

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