Establishing Peace and Conflict Studies Programs in Iraqi Universities: Necessary Conditions and Short-Term Implications
Bibliographic record
Abstract
ABSTRACT Establishing Peace and Conflict Studies Programs in Iraqi Universities: Necessary Conditions and Short-Term Implications Peace and Conflict Studies was unknown as a field of academic inquiry in Iraq when the 21st century began. Just over a decade later, formal institutional entities had been established to explore the subject at three Iraqi universities. Using a participatory action research methodology, this dissertation explores two questions: 1. What are the conditions that promote or impede establishment of a university-based program in peace and conflict studies in Iraq?, and; 2. Once established, what are possible outputs and outcomes of these programs over the first few years of their existence? This study consisted of 67 interviews, three focus groups and hundreds of hours of my first-hand observations. I argue that the presence or absence of three conditions has determined the success of efforts to establish peace and conflict studies programs at Iraqi universities: an inviting political climate; entrepreneurial or charismatic university leadership; and the availability of financial, intellectual and relational resources. I conclude that even in the first few years after a program's establishment, it is possible to observe not only tangible outputs such as students graduated, public events conducted and papers published, but also intangible outcomes such as increased awareness and understanding by students and other program affiliates of critical concepts related to peace and conflict and the creation of a platform for future learning and practice in peace and conflict studies.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".