Bibliographic record
Abstract
Anthropologists often have interesting and valuable data that remains ‘hidden’ because it does not fit easily into conventional academic publishing formats. This article suggests that it is worthwhile to make use of this hidden data for the benefit of other researchers and the study communities. To illustrate, the article describes initial efforts to create an online database of traditional weather prediction indicators derived from observations of the ecosystem. The database was started with descriptions of more than a thousand prediction indicators used in Northeast Brazil, which were collected as part of a survey of farmers and ‘rain prophets’. It is argued that such a database is important not only as part of the anthropological record, but also for the preservation of cultural heritage, and as a baseline for studies of environmental change. Some of the theoretical, practical, and ethical issues that have emerged in developing the database include: determining how much contextual information to include, obtaining translations, recruiting contributors, and properly acknowledging intellectual property. While there seems to be a great deal of enthusiasm for the idea from various sectors within and outside of academia, difficulties in securing funding for this interdisciplinary project and establishing a group of collaborators have so far presented significant obstacles.
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 | 0.043 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".