Communication effects, ethnicity, and support for secessionism in stateless nations: results from a survey experiment in Catalonia
Bibliographic record
Abstract
Over the past twenty years or so, economic and cultural interdependence has gone hand in hand with the rise of nationalism, particularly in stateless nations. For example, Catalan politics has increasingly focused on the issue of secession from the rest of Spain. As in Flanders, Quebec, Scotland, and elsewhere, the ensuing polarization of opinion creates two questions of paramount importance for social scientists: (a) How strong are individual preferences? and (b) what determines these preferences? To answer these questions, we use a custom-designed survey experiment (N = 913) which allows us to estimate the effect of frames net of confounding effects, and to determine other determinants of preferences. We find that frames matter even in polarized political times and when voters have had enough time to form their judgement on a given issue. We also detect a strong role for ethnicity, measured as the number of Catalan-speaking grandparents and language spoken at home, as well as for the geographical scope of professional activity. Our findings help challenge the economic approach to politics, whereby agents hold well-defined and constant preferences, and give support to the view that both short- and long-run constructivist elements play a crucial role in centrifugal political movements.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".