MétaCan
Menu
Back to cohort
Record W4231248356 · doi:10.32920/ryerson.14657040.v1

Good Enough to Work, Not Good Enough to Stay : A Review of the Seasonal Agricultural Worker Program

2021· review· en· W4231248356 on OpenAlexafffundabout
Janna Pushkar

Bibliographic record

Venuenot available
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsToronto Metropolitan University
FundersHuman Resources and Skills Development Canada
KeywordsWorkforceAgriculturePosition (finance)Work (physics)Government (linguistics)PoliticsBusinessCitizenshipEconomic growthPolitical scienceEconomicsEngineeringFinanceGeography

Abstract

fetched live from OpenAlex

This critical literature review examines the ways in which the agricultural sector in Canada has changed from small family farming to largely mechanized and consolidated farms thus requiring the need for the Seasonal Agricultural Worker Program (SAWP). It also finds that the program was created not only for economic but also for political reasons and it continues to function for both economic and political motivations. Since the program's inception, there has been a shift from permanent to temporary migration in many industries in Canada because foreign temporary workers such as those involved in the SAWP, labour under unfree conditions making them a reliable and disposable workforce. The denial of citizenship status to seasonal agricultural workers serves to maintain their vulnerable position in the Canadian workforce. Finally it is revealed that the program is primarily beneficial for the Canadian Government and Canadian employers. Workers and sending countries receive an economic benefit from the program as well, however this impact is much more significant for the Canadian state.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.013
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.294
Teacher spread0.245 · 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 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

Citations0
Published2021
Admission routes3
Has abstractyes

Explore more

Same topicCooperative Studies and EconomicsFrench-language works237,207