Bibliographic record
Abstract
We would like to extend our deepest thanks to the Social Sciences and Humanities Research Council of Canada (sshrc).A grant from sshrc's Insight Grant program to Adam Sneyd enabled all of the research presented in this book.As the project's principal investigator, Adam extends sincere thanks to the numerous anonymous sshrc reviewers who offered kind and insightful guidance on various iterations of his project proposal.sshrc feedback and support for his research ideas and approach ultimately guided and financed the research published in this volume.This project was inspired by the rise of calls for more socially and environmentally "responsible" approaches to governing Africa's commodities.The authors appreciate that the widespread application of these new concerns has changed business-as-usual in resource extraction and agricultural production.And in studying this new phenomenon, at the outset, we do want to emphasize that the impulse to do "better" should always be welcomed.That said, such desires are never apolitical.They can be motivated by genuinely beneficent ideals or, alternatively, by avaricious greed, or even by base-level selfpreservation instincts.As such, we would like to impress upon our readers at the outset that we have not set out to discredit any particular approaches to responsibility.We have simply attempted to understand the new politics of responsibility and to re-present our findings in a manner that captures that politics to the best of our capabilities.We cannot thank those at the forefront of global efforts to better understand responsibility and sustainability enough, including Magdalena Bexell and Kristina Jönsson at Lund University, and Judith
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.384 | 0.188 |
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".