Bibliographic record
Abstract
March 19, 2006: Sunday morning in Ma Shan county, Guangxi province. The students are preparing for their feedback presentation to the local research team and the farmers. We are having a very good field visit! Our Guangxi team represents a different kind of ‘family’ and has had different kinds of experiences as compared to last year; but just as joyful and interesting. On Friday morning we received one of the most extraordinary welcomes we ever received from farmers, with singing and so much joy… Students refrained from starting interviews with farmers right away, but instead joined the singing. The local performance group prepared a special song – an ode to the researchers, who come to the village to work together with the farmers instead of lecturing to them. The students also learned how to use threshing sticks… to make music together! Extraordinary! This was followed by lunch, followed by the first interviews, in two smaller groups. At the end of the afternoon, the local performance group danced, sang and presented a theatre play (they sang the special song again). It was wonderful. This fragment of an e-mail message provides a glimpse of the experience documented in this book. Sent to a colleague at the other end of China, accompanying another group of M.Sc. and Ph.D. students visiting a rural community in the northern province of Ningxia, it illustrates the spirit of the educational innovation process highlighted in the following pages.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.427 | 0.179 |
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".