Peace with Adjectives: Conceptual Fragmentation or Conceptual Innovation?
Bibliographic record
Abstract
Abstract What strategies can be employed to conceptualize peace? In recent years, scholars have introduced an impressive array of “peace with adjectives” in order to make sense of some of the normative and empirical underpinnings of peace. Negative, positive, everyday, virtual, illiberal, partial, insecure, relational, emancipatory, agonistic, and feminist are some of the qualifiers that have been associated with the concept. While the growing attention to conceptualization is a welcomed development, we argue that the proliferation of new terms has led to increased fragmentation in the field of peace studies. Conceptual fragmentation impedes cumulative knowledge production and generates missed opportunities for fruitful discussions across theoretical and conceptual divides. In this article, we aim to provide more clarity to our field by mapping existing peace conceptualizations and identifying the strategies employed by scholars to construct innovative new terms. In our review, we identify 61 concepts and suggest that these conceptual innovations in peace research belong to one of three analytical strategies: developing diminished subtypes, conceptual narrowing, and conceptual expansion. Building on this categorization, we make recommendations for how peace researchers can enhance clarity and deepen constructive discussions between different conceptual approaches.
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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 | 0.053 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".