Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As large-scale collaborative, cross-cultural ethnographic research becomes easier and easier to realize, certain ethnographic methods and analyses should be correspondingly more available, inviting, and accommodating. We have therefore created AnthroTools, a package for the free, open-source language R, with a variety of tools and functions suitable for both multi-factor free-list analysis and Bayesian cultural consensus modeling. Free-list data elicitation is a simple technique for ethnographic research. However, especially for cross-cultural free-list data, background preparation is considerable and often requires specific software. In addition, although current cultural consensus analysis tools offer very sophisticated analyses, they also either require specialized software or have computationally taxing methods. AnthroTools expedites these techniques, rapidly performs diagnostics, and prepares data for further analysis. In this article, we briefly discuss what this package offers cross-cultural researchers and provide basic examples of some of its functions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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 it