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Record W7098894626

Co-operatives, Credit Unions and Social Engagement in Canada Undergraduate Student Paper Submitted to the AAEA paper competition

2007· article· en· W7098894626 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiological Activity of Diterpenoids and Biflavonoids
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)Civic engagementDemographicsSocial engagementLogistic regressionSocial capitalRural areaStudent engagement
DOInot available

Abstract

fetched live from OpenAlex

Co-operatives serve as engines for local economies; generating and retaining local wealth, operating through existing social networks within communities and providing economic opportunities for local people. Is there a correlation between levels of social engagement and the presence of co-operatives and credit unions? Using the Statistics Canada GSS survey, 2003 and Environics Analytics, Business Locations data it is possible to assess the linkages between social engagement and the presence of co-operatives in Canada. Linkages between demographics (including the breakdown of rural/urban co-operatives and credit unions) and social engagement we isolated volunteerism as a dependant variable in logit regressions. It was established that: across Canada, the older people are, if an individual is female, the larger her or his household is, the less TV he or she watches, the more she or he uses internet, the higher the rate of highschool graduation, the more trusting people are of their neighbours, the more rural an area is, the more fully employed people are, the less likely they are to say no to volunteerism. Although, in all provinces the rural areas have higher levels of social engagement, social engagement is not a direct indicator for the existence of higher levels of co-operative businesses. 2

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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 designBench or experimental
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
Published2007
Admission routes1
Has abstractyes

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