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Record W2103864332 · doi:10.1177/0022002711429707

Out-Group Conflict, In-Group Unity?

2011· article· en· W2103864332 on OpenAlexaff
Théodore McLauchlin, Wendy Pearlman

Bibliographic record

VenueJournal of Conflict Resolution · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychological repressionNationalismMovement (music)Political scienceGroup (periodic table)Political economyPower (physics)Ethnic conflictEthnic groupCollective actionMobilizationSocial psychologyEconomic systemSociologyPsychologyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

Does repression increase or decrease unity within ethnic or nationalist movements? Conventional wisdom lends itself to two contradictory predictions. On one hand, it is said that conflict with an out-group is the surest path to unity in an in-group. On the other hand, repression exaggerates the gap between radicals and moderates in a movement. Challenging both views, this article argues that repression amplifies trends in cooperation or conflict existent in a movement before the onset of repression. All movements have some institutional arrangement, meaning a set of procedures and relationships that structure decision making and behavior. These “rules of the game” distribute power within the movement, and thus favor some actors over others. Repression disrupts the equilibrium of these institutions, after which the members might engage in either more cooperation or more conflict, depending on the level of satisfaction with preexisting institutional arrangements. The authors illustrate these propositions through comparative analysis of four repression shocks from two nationalist movements: the Kurdish movement in Iraq and the Palestinian national movement.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.012
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.101
GPT teacher head0.332
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations102
Published2011
Admission routes1
Has abstractyes

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