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Record W4285464764 · doi:10.32920/ryerson.14656422.v1

Exploring the social and economic development impact of the Canadian Seasonal Agriculture Workers Program (CSAWP) for Jamaican migrant workers, their families and communities

2021· preprint· en· W4285464764 on OpenAlexaffabout
Paulette Carol Wright

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnthusiasmEconomic growthImmigrationAgricultureConsumption (sociology)Political scienceSocioeconomicsSociologyGeographyEconomicsSocial sciencePsychology

Abstract

fetched live from OpenAlex

The enthusiasm of immigrant sending countries around migration and development hinges on the fact that the flow of money, knowledge and universal ideas can have a positive effect on development in these countries. The Canadian Seasonal Agriculture Workers Program (CSAWP) was established in 1966, most of the Social Science literature on this program has emphasized its exploitative and problematic aspects. Without dismissing the significance of the focus and results of other research, this paper examines the social and economic development impact of this program on households and communities in Jamaica. Research done by academics and an analysis of Jamaica‟s newsprint media done for this research reveal that the CSAWP has had positive development impacts. Findings suggest that the program is delivering social and economic benefits to migrant workers and their families. It has increased income, consumption, child schooling and improved health care. In addition to improving the standard of living for migrant workers and their families, the CSAWP has additional benefits at the community and national levels.

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)0.002
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: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.301
Teacher spread0.210 · 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
Published2021
Admission routes2
Has abstractyes

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