Bibliographic record
Abstract
Are conflicts dynamic processes? Though seemingly answered with a simple yes, the tendency at times to analyze and thereafter market complex conflict processes based on discrete categories of violence exposes a shortcoming in current analyses of conflict throughout the African continent. This not only suggests the need to problematize the very analytical frames employed to understand conflicts and how we respond to violence, but also interrogate why certain frames are applied selectively despite cross case similarities or why particular violence metanarratives are superimposed on conflict dynamics with little regard for how subnational processes may "map onto" or undermine macro conflict dynamics over time. 1 While conflict framing is critical for building and developing theory as well as informing conflict prevention and resolution strategies, it has the potential to narrow our vision, obscuring the evolving nature of conflicts, and impeding peacebuilding efforts. Concealing pertinent factors driving conflict may not be the only unintended consequence of conflict framing. Perhaps more pressingly, the way in which conflict and violence are framed, and the approaches taken, thereafter, may function to facilitate violence escalation rather than mitigation. This poses a critical challenge to scholars and practitioners studying political violence.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".