Framing and Contesting a Revolution: Identity Construction, Gender, and Rebel Group Cohesion in Columbia
Bibliographic record
Abstract
The literature on rebel group cohesion and desertion from armed groups offers a variety of explanations for patterns of disengagement from armed violence, including government pressure, in-group violence, disillusionment in the group's cause, networks, and trauma.But most of the disengagement literature focuses on men who have deserted their groups, with much less information on those who stay until ordered to disarm-and almost no analysis on women who disarm.The lack of comparative analysis between deserters and loyalists limits what we understand or can predict about rebel group cohesion.In addition, this literature has failed to adequately explore the role of gender norms, even though hypermasculinity, narratives of brotherhood, and feminization of the enemy are well-established mechanisms for increasing troop cohesion in militaristic groups.Based on over 100 in-depth interviews with former guerrillas and paramilitaries in Colombia, this dissertation argues that framing contests and related identity constructions are critical in insurgencies and civil war, and that the outcome of these contests influences individual decisions to disengage from violence and the experiences of ex-combatants after demobilization.Second, I argue that how these competing frames operationalize gender norms influences not only troop cohesion but also the way combatants calculate their investments in the group and possible alternatives.As a result, even recruits that are not fully committed may stay for lack of alternatives.Conversely, recruits may desert their group only to face the stigmatizing consequences of government narratives in civilian life.This study examines what variables produce these outcomes, emphasizing the role of framing contests and arguing that ignoring gender in rebel group cohesion has left a significant gap in our understanding of both desertion and post-conflict reintegration.my access to these sites, which were the jumping off point for this work.I am indebted to my hard-working transcribers, Alejandro Reverend and Jorge Soto (without whom I would no doubt still be transcribing), and to my beloved Spanish teacher, Mauricio Hoyos and his family, who ensured that I was well-versed in Colombian slang and provided a place to rest in the midst of my intense fieldwork.And I do not even know how to thank Alejandro Carlosama, who worked beyond all expectations as my research assistant, fixer, and confidante.Alejo, estoy muy agradecida por lo que has hecho y siempre te tendré a ti y a tu familia en mi corazón.Many wonderful friends kept me sane during this process, especially those who tolerated hearing about my dissertation endlessly: Julie Stonehouse, Maya Dafinova, Gaëlle Rivard Piché, Mia Schöb, and my runner girl gang.I will also always be grateful to my parents for fostering curiosity and a lifelong passion for learning, and for flying across the country many times to help with childcare.My dear children, Jesiah and Calia, showed incredible courage and tenacity throughout this long process and endured a lot of upheaval.And my husband, Jon, whose unfailing loyalty, flexibility, and encouragement-even when I made highly questionable decisions-allowed all of this to happen.Thank you for being a rock when I was the storm.Lastly, I owe my life to someone whose name I do not know.In 2012, a
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".