Bibliographic record
Abstract
Acknowl edgmentsThis book would not have been pos si ble without the generosity, kindness, and care of a number of people who helped me cultivate my ideas and develop my voice as a writer and critical thinker.Writing this book has been a meandering journey across vari ous countries (South Africa, the United States, and Canada) and it represents the many people-professors, colleagues, mentors, friends, family, and lovers-who have touched my life in vari ous ways.First and foremost, I would like to express my deepest gratitude to Xavier Livermon and Vanita Reddy, whose mentorship and guidance have certainly shaped this book in ways I could not have imagined when I first embarked on this journey.Xavier, you are prob ably the only other person who has read every single draft of this book, and I continue to be amazed by your generosity and dedication to this proj ect and to me as a person and scholar.I thank you for the many hours (and unseen labor) you have dedicated to this proj ect, your encouragement when I felt that I was incapable of doing one more round of revisions, and the space you created for me to be emotionally vulnerable.You grasped the importance of this proj ect early on and I thank you for guiding me and modeling a form of mentorship based on a practice of care and generosity.Since Vanita and I met in Bloomington, Indiana, she has mentored me through the job market and become an impor tant interlocutor who has shaped this book in impor tant ways.I want to thank you, Vanita, for holding me accountable, for the many hours you spent reading and rereading the introduction of this book, and gently encouraging me to slow down.Through this process you have taught me such impor tant writing skills, and the clarity of this text is directly attributed to your (unseen) labor and your mentorship throughout this process.Xavier and Vanita, thank you.You both have provided models of mentorship and
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.290 | 0.244 |
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".