Voting behaviour in deeply divided societies: partisanship and ethnic voting in the hills of Manipur
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Voting patterns in the hills of Manipur show high ethnic voting indicating extremely partisan attachments. The salience of ethnicity in voting preference is reinforced by conflicts in the past. Periodic elections only exacerbated such divisions through partisan mobilisation and competition for representation along ethnic lines. Cross-ethnic voting is prevalent among groups wherein cousinage alliance cannot be forged due to past conflicts. The neck and neck competition for political representation among ethnic groups has sidelined the democratic rights of individuals over partisan group interests. In such deeply divided societies ensuring individual rights is the challenge of democratic governance, and the only viable solution to this pervasive problem appears to be a constitutional reform with the aim of having a more inclusive representation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it