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
Conflicts that are reported as being between ethnic groups are often described as “ethnic conflicts.” The implication is that such conflicts belong to a general type of ethnic conflict with certain repeated and predictable features. This type of conflict is seen as being motivated by ethnic sentiments, as being grounded in deeply set hatreds, and as being virtually inescapable. By applying the epithet “ethnic,” it is as if the conflict were already explained. However, there are many reasons to be suspicious of these implications. Ethnic groups presently embroiled in fierce conflict may have been, at a previous point in time, peacefully co-existent. Frequently, the very lines of ethnic difference become blurred through intermarriage and cultural change. Therefore, in order to understand conflict described as “ethnic” we need to uncover the reasons why (in a given conflict situation) there is heightened awareness of ethnic difference. Then we need to explain what I have termed “the conditions of ethnicity,” that is, the external conditions which lead to severe conflict; and those external circumstances that make it likely that the conflict will follow lines of ethnic differentiation. Two of these conditions are the strength of the state system and the ability of the state to manage ethnic conflict.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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