“Who are we without the war?”: The evolution of the Tamil ethnic identity in post-conflict Sri Lanka
Bibliographic record
Abstract
Studies of post-conflict societies have often focused on inter-ethnic group dynamics following the end of conflict, specifically the process of reconciliation between groups, or resurgence of violence across groups. This paper focuses on intra-ethnic differences with respect to defining ethnic identity. This paper will examine how the end of the Sri Lankan ethnic conflict created cleavages amongst Sri Lankan Tamils with respect to how they define their ethnic identity and their ethnic group. Drawing upon 66 semi-structured interviews conducted in three regions of Sri Lanka, this paper presents three perspectives that were held among Tamils in post-conflict Sri Lanka. The first perspective was that the end of the ethnic conflict led to a loss in the fundamental tenets of the Tamil ethnic identity. The second perspective considered the promotion of a distinct Tamil ethnic identity to be a gateway to conflict. The third perspective articulated that the end of the ethnic conflict meant that the Tamil ethnic identity could move forward in a more cosmopolitan direction. The findings of this study demonstrate the importance of considering the social construction of ethnic identities, and their implications on post-conflict reconstruction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".