Bibliographic record
Abstract
This book has been a journey, both personal and professional, across time and continents.During my first few weeks of fieldwork in Ghana I encountered the adage "if you want to go far, go together; if you want to go fast, go alone."It could not be more true.This book began to germinate in 2004 when I moved to Botswana to work in the field of hiv and human rights -that experience continues to shape how I think, what I study, and who I feel accountable to.Ke a leboga thata to wusc, bonela, and everyone involved for helping to set me on this path.Bookending this journey (literally and figuratively), it is critical to acknowledge that this manuscript was largely prepared during two enriching and supportive postdoctoral fellowships: one at the Centre for Human Rights, in the Faculty of Law at the University of Pretoria, and another in the Department of Political Science at Dalhousie University.My sincere appreciation to the 145 people who gave me their time and their trust by agreeing to be research participants.Thank you for your generosity.I have learned so much from each of you.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.324 | 0.250 |
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".