Bibliographic record
Abstract
We would like to thank the many individuals who took the time to fill in our surveys and to talk with us about their experiences in the contemporary labour market.As we hope the following chapters demonstrate, in addition to providing us with data, their insights into their experience as workers contributed greatly to the development of our theoretical framework and to our understanding of what it means to be working both with, and without, commitments.A number of our colleagues were closely involved with this project at various stages and will recognize their valuable contribution to his volume.In particular, Andy King (department leader in Canada of the United Steel Workers Canadian National Health, Safety and Environment Office), joined our research group in its early stages, and worked closely with us for several years.He was a key participant in our discussions about how to deal with the relationship of precarity to health, and played an important role in shaping our initial thinking.Michael Polanyi was also involved in many of those early discussions, and provided useful comments on the research as the project unfolded.This study originated in a research project on precarious employment housed at York University and lead by Leah Vosko which provided a supportive environment for some of our ideas.Cindy Gangaram was instrumental in coordinating our work during this initial period, and suggesting we use a Canada Post service for distributing surveys to workers in particular Toronto neighborhoods.As the study grew, a number of honours and graduate students at McMaster University worked with us on the survey and interview components of the research.Special thanks go to them, especially to Sarah Declerk and Emily Watkins preface vii
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.295 | 0.170 |
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".