Sara Cobb, Speaking of Violence: The Politics and Poetics of Narrative in Conflict Resolution.
Bibliographic record
Abstract
A s I was finishing Sara Cobb's book, CBC News (Paris 2014) reported a statement by Canadian Foreign Affairs Minister John Baird, commenting on increasing violence between Israel and the Palestinians.'"The buck stops with Hamas,' Baird told reporters in Ottawa.'Hamas started this bloodshed.Hamas can end it.'"That way of framing the issue--Baird's narrative of the conflict's origins and its possible solution--exemplifies what I believe Cobb understands as a "bad story".Developing ideas from Hannah Arendt, Cobb writes: "there are narratives that, in and of themselves, shut down alternatives to themselves ... they tell a narrative of wrongdoing and provide an account of violation, but this narrative perpetuates and deepens the kind of discourse that contributes to destroy rather than open public debate and deliberation" (37).Cobb is Professor of Conflict Analysis and Resolution at George Mason University, located -significantly for the book she has written -in Washington, D.C.She is a practitioner of conflict resolution, but this particular book is less a guide to practice than "an effort to provide a theoretical foundation for narrative practice in conflict resolution that links it to critical theory and thus builds a normative framework on which we can create/describe an ethics for critical narrative practice" (227).In line with that objective, Cobb delays presenting a full case study of conflict resolution until late in the book, around page 200.That is not the editorial decision I would have recommended, but it reflects her intentions for the book.Readers might want to begin with this case study in Chapter 7 and then return to the beginning to unpack the theory that underlies the practice.Cobb's writing is grounded not only in extensive practice but also broad scholarship.Her eclectic version of critical theory begins with Hannah Arendt but then turns to French authors.Lyotard's idea of differend, "the space of suffering that cannot be described in the current idioms available to the Self" (153) figures significantly, as does Foucault's conceptualization of discourse.Sociological theories of conflict appear rarely if at all, but that should be more reason for sociologists to attend to Cobb's writing.Her broadly interdisciplinary approach opens
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".