Bibliographic record
Abstract
The papers that comprise this thesis address two prominent issues affecting workplace safety. The first of the three studies looks specifically at the trucking industry, examining how wage schemes tied into delivery times affect the likelihood of a truck driver being involved in an accident. Using several estimation techniques including probit and path analysis on data collected from the Sloan Foundation Trucking Industry Program Driver Survey 1997-1999 a direct relationship was found between sleep depravation and the likelihood of an accident. In turn, the drivers that were most likely to fall asleep were those whose wages were either tied into their delivery schedules or who had financial incentives to deliver their loads on time. The remaining two papers investigate workplace violence. One paper examines the length of time away from work for victims of workplace violence. Hazard models were estimated using data from (non-fatal) work-related injuries that were the result of workplace violence between 1996 and 1999 in the Canadian province of Ontario drawn from the administrative records of the Workplace Safety Insurance Board (WSIB). The results of the study indicate that the greater the severity of the injury, as measured by total health care expenditures, the longer the absence from work. Furthermore, the most violent acts are associated with the largest decreases in the hazard rate and result in the longest absences. Finally, there were statistically significant differences in the duration of work absences by occupation, holding other factors constant. Whether it is the pressure of payment based on unreasonable performance, or working under the threat of violence, the information provided in these studies help to quantify some of the consequences of demanding and dangerous work-related experiences. The final workplace violence paper uses the same Ontario WSIB data to calculate some of the financial costs associated with acts of workplace violence. A descriptive summary of the total claim payments broken down wage benefits and healthcare payments indicate that for the most part the highest dollar amounts were associated with the most severe forms of physical violence, shootings, stabbings, and sexual assaults.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".