How Policy Narratives from Foreign Countries Shape Domestic Perceptions
Bibliographic record
Abstract
My central objective is to uncover if ‘narrative transference’ occurs and, if so, how it influences individual views towards issue importance and public resource allocation. I would also like to explore potential moderators, most importantly; a potentially negative relationship between “cultural distance” and narrative transference. My current working definition of narrative transference is defined as the process by which a person or group ‘imports’ a narrative from another place or context into their current, different place or context. For example, a person living in Austria may become increasingly concerned about obesity in their country after reading a news article about the growing problem of obesity in Germany whether or not obesity rates are changing in Austria. I have identified a gap in the literature regarding the concept of ‘narrative transference’ which may take place between any social groups or individuals. I will specifically be looking at country level ‘social-groups'. It does not appear that the concept is well-studied in the literature. I will conduct a survey with Canadian participants using a between-subjects research design testing the influence of narratives from other countries on participants' perceptions of that issue in Ca
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".