Perceptions, expectations, motivations: Evolution of Canadian views on the EU
Bibliographic record
Abstract
This article proposes a conceptual model that factors external and internal drivers behind external perceptions in IR and allows to trace their interaction across geographical distances argued by social identity theory (Moles and Rohmer, 1978) and evolution across historical distances defined by historical geography (Braudel, 1989). This article used the case of Canadas perceptions of the EU to demonstrate the model in action and trace the ‘mental mapping (Didelon-Loiseau and Grasland, 2014) of the EUs images through the perceptions of EU-Canada relation over time. Informed by the tripartite paradigm of the influential factors behind external perceptions of the EU: endogenous, exogenous and global (Tsuruoka, 2006; Chaban and Magdalina, 2014), the article offers a model that goes beyond this logic in an innovative way. It considers a geo-temporal matrix of vantage points that shape perceptions. To demonstrate the model in action, this article reviews existing research on perceptions of the EU in Canada focusing on the key works and their findings in this field over the last decade.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".