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Record W2486283824 · doi:10.20381/ruor-5109

Ethnic Conflict and Contemporary Social Mobilization: Exploring Motivation and Political Action in the Sri Lankan Diaspora

2016· dissertation· en· W2486283824 on OpenAlexaboutno aff
Martha Elizabeth England

Bibliographic record

VenueuO Research (University of Ottawa) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaEthnic groupMobilizationPoliticsPolitical mobilizationEthnic conflictPolitical scienceAction (physics)Social mobilizationPolitical actionGender studiesSociology

Abstract

fetched live from OpenAlex

Members of the diaspora are conflict actors with an agency that is important to include in conflict theories and analysis of international relationships. Scholarship suggests its origins, and thereafter changes in the conflict cycle effect decision-making and mobilization in the diaspora, but the conditions and mechanisms that inform these processes are undertherorized. The Sri Lankan conflict and its Toronto based diasporas are used to explore processes of diasporization and mobilization in the context a changed political landscape. A series of semi-structured interviews and a short survey asks respondents to assess their motivations for mobilization. The comparative work is within and between ethnic groups. New Institutionalism underscores this project. Butler’s (2001) epistemology, Brinkerhoff’s (2005) identity-mobilization framework, the political process model and insights from the New Social Movement literature are used to situate politicized identities and political activism directed toward the homeland. Attention is paid to factor processes.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0080.010
Scholarly communication0.0080.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.385
GPT teacher head0.430
Teacher spread0.045 · 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 designQualitative
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

Citations0
Published2016
Admission routes1
Has abstractyes

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