Peace Studies and Conflict Resolution: The Need for Transdisciplinarity
Bibliographic record
Abstract
Peace studies seeks to understand the negation of violence through conflict transformation, cooperation and harmony by drawing from many disciplines, including psychology, sociology and anthropology, political science, economics, international relations, international law and history. This raises the problem of the complementarity, coexistence and integration of different systems of knowledge. In fact, all of the human and social sciences are products of the post-Westphalian state system and so reify the state and its internal and international system and focus on this as the main source of political conflict. Conflicts, however, can arise from other distinctions involving gender, generation, race, class and so on. To contribute to peace building and conflict resolution, the social sciences must be globalized, developing theories that address conflicts at the levels of interpersonal interaction (micro), within countries (meso), between nations (macro ), and between whole regions or civilizations (mega). Psychiatry and the "psy" disciplines can contribute to peace building and conflict resolution through understanding the interactions between processes at each of these levels and the mental health or illness of individuals.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.018 | 0.149 |
| Scholarly communication | 0.034 | 0.058 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".