Bibliographic record
Abstract
This project has truly been a journey, from idea, to prospectus, to dissertation, to book.Along the way I have compiled a very long list of people and organizations to thank for their contributions to my work and my life.My own journey to academia followed a route that was far from traditional.Before returning to school to study for a PhD, I spent several years working as a professional dancer, model, and actress in Toronto.Although I loved the energy and camaraderie of working with other performers (especially all my friends in the Raptors Dance Pak), I decided to go back to school for radio and television production at Ryerson University in the hopes of establishing a more lucrative and "legitimate" career on the other side of the camera.Realizing that I lacked the passion for media production, I dropped out of Ryerson and spent a year working in the public relations department of what was then CTV Sportsnet (now Rogers Sportsnet).In many respects this book is a logical outgrowth of these experiences.Working in the world of professional sport and entertainment has been an important source of insight when it comes to exploring representations of race and gender in popular culture.Part of my goal in reexamining Jack Johnson's story is to take seriously the political conversations stemming from this world.This book honors the many vibrant people who were central to this part of my life, including Greg and Chris Johnson, who introduced me to the sport of boxing.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.366 | 0.266 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".