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
Teaching has attracted growing research attention in studies of human and animal behavior as a crucial behavior that coevolved with human cultural capacities. However, the synthesis of data on teaching across species and across human populations has proven elusive because researchers use a variety of definitions and methods to approach the topic. I propose a novel method for the study of teaching behavior to be used across disciplines and populations toward such a synthesis: a teaching ethogram for animal and cross-cultural human research (TEACH). This article compares the results of the TEACH method with interview and time allocation data from the same study populations on Yasawa Island, Fiji. The TEACH method better matches the emic view of teaching as playing a role in children’s learning in Fiji, in contrast to the time allocation method. The TEACH method also produces quantitative data with greater behavioral detail than the other methods. This feature is particularly important for the usefulness of the TEACH method in making broad comparative data possible.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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