Bibliographic record
Abstract
This presentation provides an overview of ongoing research on the agri-food industry’s messaging surrounding temporary foreign workers (TFWs). TFWs are a central and growing component of Ontario’s agri-food workforce, providing a source of reliable seasonal labour as the industry anticipates continued shortages of local workers over the next decade. Simultaneously, agricultural migrant labour has been a topic of contention in recent years, with academic research and news articles highlighting the potential for exploitation and negative health outcomes faced by TFWs. Several groups representing the agri-food industry have taken measures to influence public opinion on these matters through media campaigns featuring websites, social media, newspaper articles, and educational materials. The research featured in this presentation addresses an absence of academic literature on this subject through critical discourse analysis. This project involves the analysis of written and oral texts on TFWs produced by agri-food groups in order to engage with industry discourse on migrant labour. Key insights include the central arguments presented by these texts, the rhetorical and stylistic choices made, and the ways these texts align or conflict with established academic literature. This research will have implications for the formation of policy surrounding agricultural TFWs and public discourse on this subject.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".