Bodies and Documents: The Material Impact of Collaborative Information- Sharing Within the Seasonal Agricultural Worker Program
Bibliographic record
Abstract
This study examines information-sharing practices within the Seasonal Agricultural Worker Program (SAWP), focusing on the program as it is administered within Ontario. I analyze 61 documents for their content, codification of stakeholder relationships, and discourse regarding the program. Documents were selected based on their creation, use, or circulation within Ontario, and based on the likelihood that at least one stakeholder group would look to the document for (what they perceive to be) reliable information. Documents include, for example, SAWP contracts, webpages describing program requirements, and e-pamphlets on workplace safety and accessing services. Document analysis was supplemented by interviews with industry and service provider experts, which guided interpretation of documents’ significance. I argue that documents function as material actors, alongside (and sometimes beyond) human actors, and make physical impact on SAWP bodies and realities. Documents construct and uphold neoliberal structures surrounding the program by contributing to the creation and sustaining of incomplete, labour-centric individuals. Through consistent sharing of narrow, “work” information, and the rare inclusion of more well-rounded, “non-work” knowledge, documents subtly discipline the boundaries of acceptable and unacceptable communication. In doing so, material actors (alongside other SAWP actors) perpetuate a foreign worker program which does not consider the varied, complex needs of whole persons but, instead, treats them as disposable labouring bodies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".