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
ABSTRACT In India, as elsewhere, voters and electoral observers refer to powerful, emerging political trends as “waves,” such as the “Modi wave” that brought Prime Minister Narendra Modi to power. In rural South India, during the run‐up to the 2019 national elections, how did people read signs of political waves, and how did voters align themselves with these signs as they circulated across media forms? A “wave theory” analysis finds that rural voters in the state of Tamil Nadu assess electoral chances across various factors—including candidates’ cash flow, crowd behavior, caste calculations, and professional analyses of polling data. People take electoral positions within ambient and layered ecologies of information, in which everyday speculation about political fortunes is situated alongside formalized methods of analysis, calculation, and prediction. And because electoral waves are experienced locally but often carry energy from afar, they constitute both a force and a fluid medium of convergence. [ elections , democracy , prediction , crowds , money , caste , media , Tamil Nadu , India ]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it