Bibliographic record
Abstract
Preliminary – Please check with authors for most recent version before any citation Comments are welcome. This paper would not have been possible without the very generous help of Indira Hirway, Director and Professor of Economics, Centre for Development Alternatives, Ahmedabad, India. Professor Hirway was instrumental in the design of the Indian Time Use Survey and her assistance in obtaining and interpreting the micro data from this survey is deeply appreciated. We would also like to thank Will Gibbons for his excellent work as research assistant in helping prepare this paper and the Social Sciences and Humanities Research Council of Canada for its financial support under Grant 410-2001-0747. 2 In 18.6 % of rural Indian households in 1999, somebody (usually female) had to devote an average of 47 minutes per day to fetching water – time which could have been put to more productive use. The provision of tap water illustrates two crucially important and linked aspects of the development process – the problem of organizing collective action
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.709 | 0.588 |
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".