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Record W4392928051 · doi:10.32920/25417222

On Reckoning and Renewal: A Discourse Analysis of Migrant Farm Worker Coverage in Alternative News Media

2024· preprint· en· W4392928051 on OpenAlexafffundabout
K. Robinson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsToronto Metropolitan University
FundersEmployment and Social Development CanadaAgriculture and Agri-Food CanadaInternational Labour OrganizationWorld Health Organization
KeywordsJournalismScholarshipCitizenshipContext (archaeology)Public relationsDigital mediaNews mediaMigrant workersPolitical sciencePoliticsSociologyWork (physics)Alternative mediaMedia studiesEconomic growthGeographyLawEngineeringEconomics

Abstract

fetched live from OpenAlex

This Major Research Paper seeks to assess alternative media coverage of migrant farm workers in Canada during the COVID-19 pandemic. Extending upon what Callison and Young (2020) call a “digital reckoning” taking place in Canadian journalism, I employ the theoretical framework of political economy of communication (PEC) to interpret the organizational practices and funding models of the shifting mediascape. Following in the tradition of PEC, I ask how alternative media in Canada are covering migrant farm workers and worker programs during a pandemic, and to what extent these publications are addressing larger systemic issues in their coverage. A critical discourse analysis of two print magazines and four digital media outlets finds common themes surrounding precarious work, exploitation and the need for migrant workers to access a pathway to citizenship, but relatively less context into systemic issues. A broader assessment of the journalism industry encourages future scholarship of alternative media in Canada.

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)0.010
metaresearch head score (Gemma)0.020
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.007
Science and technology studies0.0190.027
Scholarly communication0.0170.008
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.364
Teacher spread0.322 · 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
Published2024
Admission routes3
Has abstractyes

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