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Record W2788032158

Understanding the Absence of Unionized Workers in Rural Alberta, Canada

2018· article· en· W2788032158 on OpenAlexaffvenueabout
Bob Barnetson

Bibliographic record

VenueJournal of rural and community development · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCollective bargainingEmbeddednessPolitical scienceHumanitiesGeographySociologyArtLawSocial science
DOInot available

Abstract

fetched live from OpenAlex

This preliminary study spatially locates 333,881 unionized workers in the Canadian province of Alberta, identifying that a disproportionate percentage of unionized workers are located in urban centres and in bargaining units of greater than 100 members. Most unionized rural workers are found in large, public-sector bargaining units. Interviews with trade unionists suggest possible explanations for this pattern, including the unequal distribution of capital, rural workers’ spatial embeddedness, unions’ preference for large bargaining units, and the differentially and negative impact of weak labour laws on rural workers. Keywords: unions; rural; labor geography; Alberta; Canada ----------------------------------------------------- Cette etude preliminaire localise geographiquement 333 881 travailleurs syndiques dans la province de l'Alberta, au Canada, identifiant qu'un pourcentage disproportionne de travailleurs syndiques sont localises dans les centres urbains et dans les centres de negociation. Des entrevues avec des syndicalistes suggerent des explications possibles a ces tendances, incluant la repartition inegale du capital, l'integration spatiale du travailleur rural, la preference des syndicats pour de grandes unites de negociation, et les impacts differentiels et negatifs des lois du travail inadaptees aux travailleurs ruraux.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.715

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 studies0.0010.000
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 head0.034
GPT teacher head0.223
Teacher spread0.189 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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