La integración de la teoría y los métodos: un nuevo enfoque para la investigación comparativa de las divisiones sociales La integración de la teoría y los métodos: un nuevo enfoque para la investigación comparativa de las divisiones sociales
Bibliographic record
Abstract
This paper proposes a new method for comparative research on social cleavages that integrates the major approaches to voting behaviour –the sociological approach, rational choce, and party identification– under a single methodological and theoretical framework. The approach is unique in that it models the social bases of vote and attitudes separately and explicitly compares these separate models across regions. We apply this method to the major regions of Britain and Canada, and find striking cross-national and cross-regional similarities in the effects of social identities on attitudes, but considerable diversity in the effects of social identities on vote. These results suggest the importance of regional political contexts in determining the effects of social groups on voting. We conclude by arguing that the method developed in this paper can have wide applicability for comparative social research. Este artículo propone un nuevo método para la investigación comparativa de divisiones sociales en el que se integran los principales enfoques sobre el comportamiento electoral –el enfoque sociológico, la elección racional y la identificación partidista– en un único marco metodológico y teórico. El enfoque es único ya que modeliza las bases sociales del voto y de las actitudes por separado, y compara estos modelos separados en distintas regiones. Aplicamos este método a las principales regiones de Inglaterra y Canadá, y encontramos sorprendentes similitudes transnacionales y transregionales en los efectos de las identidades sociales sobre las actitudes, pero considerables diferencias en los efectos de las identidades sobre el voto. Estos resultados nos indican la importancia que tienen los contextos políticos regionales en la determinación de los efectos de los grupos sociales sobre el voto. Concluimos defendiendo que el método desarrollado en éste artículo puede tener una amplia aplicación en la investigación social comparativa.
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 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 | 0.042 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".