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
It may help to understand human affairs to be clear that most of the great triumphs and tragedies of history are caused, not by people being fundamentally good or fundamentally bad, but by people being fundamentally people.(Pratchett and Gaiman 1990, 39) For decades prior to the 1980s, when our ability to read ancient texts became more fully developed, the narrative of the ancient Maya as peaceful stargazers dominated and even directed early studies based in ethnography, ethnohistory, art history, and archaeology (best exemplified in Morley 1946; see discussions in Sullivan 2014; Webster 2000; Wilk 1985).Alongside more general narratives surrounding the "noble savages" of the Americas (Deloria 1969; Otterbein 2000a), these biases served to limit earlier considerations of conflict in the ancient past.Since the 1980s, significant contributions to the study of ancient Maya-and, more generally, Mesoamerican-conflict have appeared in peer-reviewed articles, books and book chapters, and popular media.Although this volume is intended as a follow-up to previous scholarly contributions, such as Brown and
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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