The Complexities of Unknowns: Knowledge Contestations and Occupational Disease Recognition
Bibliographic record
Abstract
In decision-making processes, competing knowledge claims create tensions, contestations, and negotiations between various social actors in their efforts to reach a decision.While attention to questions of knowledge (such as how certain knowledge gains legitimacy and authority) are useful in examining the dimensions and stakes of knowledge contestations, broadening the analysis to consider the complex role of unknowns can provide fruitful and nuanced insights into these contestations.Through a qualitative methodological research design, in this dissertation I focus on knowledge contestations in relation to the challenges of recognizing occupational diseases in the context of the Ontario workers' compensation system.The research questions that drive this investigation are as follows: (1) how do unknowns complicate knowledge contestations, specifically those surrounding the recognition of occupational diseases; (2) how do various types of knowledges and unknowns become mobilized in these recognition processes; (3) what counts as evidence in recognition processes, and what role does evidence play in supporting various knowledge claims; and (4) how do social and political factors influence the recognition of occupational disease?In exploring these questions, I primarily draw on three theoretical resources: new materialism, sociology of knowledge, and ignorance studies.I find that multiple dimensions of unknowns play a pivotal role in knowledge contestations over occupational disease recognition.The forms that such unknowns tend to take complicate and obscure connections between occupational factors and the development of disease.The mobilization of unknowns in contestations over disease recognition presents further challenges due to conflicting economic and other interests of the various social actors involved in these decisionmaking processes, as well as the broader influence of the dominant biomedical model in knowledge about disease and the body.A spotlight on FOI requests and archival methods………………………………52 FOI requests……………………………………………………………...52 v Archival methods……………………………………………………...…57 Case selection: occupational disease and Ontario's workers' compensation system……………………………………………………………………….61A note on methodological considerations in researching unknowns………..…...63 Reflexivity and epistemology……………………………………………………65 Conclusion……………………………………………………………….………71Chapter Three: Historical and Legislative Framework of Occupational Disease and Workers' Compensation in Ontario ……………………………………..….………..73 Legislative framework: Workplace Safety and Insurance Act (WSIA)…………74 Occupational disease and workers' compensation: historical overview…………82 Before the modern workers' compensation system (mid-1880s to 1914)……………………………………………………………....82 Development of WCB and growing concerns about occupational disease (1915 to 1970)……………………………………………………………86 The tumultuous years (1970s to mid-1990s)………………………….…91 Challenges to occupational disease recognition (mid-1990s to present)…………………………………………………………...101 Conclusion……………………………………………………………………...
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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 | 0.066 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.022 | 0.105 |
| Scholarly communication | 0.029 | 0.041 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".