National Self-Image as a Justification in Policy Debates: An International Comparison
Bibliographic record
Abstract
Abstract In national policymaking speakers commonly refer to models and policies adopted elsewhere as a means to justify a bill. However, empirical analysis of parliamentary talk in eight national parliaments (Argentina, Canada, Chile, Finland, Mexico, Russia, Spain and the USA) reported in this article showed an interesting relationship between two types of justifications: of the eight countries compared, the ones that rank lowest in references to the international community as means to justify or criticize domestic legislation rank highest in the frequency with which national self-image is evoked. Yet these two types of justification exist in the same debates, because the occurrence of both of these discourses correlates with debate length. The variation is due to differences between political cultures: in countries like Argentina and the USA, where national self-image is employed most frequently, speakers have at their disposal stories that bolster beliefs about the country’s uniqueness. In contrast, in the parliaments of Canada and Finland, where references to national self-image are most infrequent, references to the country’s history are rare, and talk about national self-image is entwined with international references.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".