Bibliographic record
Abstract
It is incredibly humbling to write these words of thanks.They form part of an ongoing recognition that any author works in an ecology of people who contribute in ways big and small.I am acutely aware that the acknowledgments are where a great deal of otherwise invisible labour and care of social reproduction are made public: thank you, one and all.The shortcomings, of course, remain mine.My deepest thanks go to people in Vanuatu.I would like to thank Ralph Regenvanu, who was finishing his term as director of the Vanuatu Kaljoral Senta (VKS) at the time of my fieldwork, along with Henline Mala and Evelyne Buleigh at the VKS for their support.As well, I thank the members of the Vanuatu Cultural Research Council for approving my research permit.Profound thanks to Paramount Chief of Pango Rolland Maseiman Kalwatman, who granted permission for me to conduct ethnographic research in 2010.There are so many people who supported me and my little family in big and small ways during my fieldwork, and I am grateful and thankful to them all for sharing their experiences, knowledge, and hospitality.Some people whom I was fortunate to come to know in Vanuatu need special thanks
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.254 | 0.217 |
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".