Identities and Federal Dynamics in Multinational Societies: comparisons within and between Belgium and Canada
Bibliographic record
Abstract
Identities matter and this is especially true in multinational societies. In such societies, political identities play a chief variable in the political dynamics of the country. Identities receivetherefore much attention in most studies on this topic. In this perspective, communities are often seen as monolithic blocks at war with each other, along ethnic-linguistic lines. However, there is a missing link between identities and federal dynamics: it should be explored how identities shape the federal dynamics and how the federal dynamics shape identities. In Belgium and Canada, two multinational federations, the federal dynamics is much influenced by abipolar dynamics between the two main ethno-linguistic groups, which may stir fruitful comparisons and insight for the understanding of multinational federations. In both countries, identities and federal dynamics are intrinsically related to each other. Yet, their current political situation is quite different. While Belgium is stuck in a deep institutional crisis, Canada has now entered a period of – more or less – stability; even though the potential for tensions has not disappeared. The different path may be explained by the interactions between identities and federal dynamics within and between the two main linguistic groups. To explore these interactions, four citizens panels were organised in Montréal (Quebec), Kingston (Ontario), Liège (French-speaking Belgium) and Antwerp (Dutch-speaking Belgium). Each panel gathered 20-30 citizens for a half-day group discussion and thus provides insightful qualitative data. More specifically, different “identity triangles” – the identities of citizens in terms of “me”, “us”, and “them” – come out this original research and these triangles illuminate different perceptions and preferences for the future of their country. The results show the identity triangles within Canada are more dissimilar than within Belgium. It also reveals the proximity of some profiles between the two countries. Although a quantitative research would be needed to offer a greater leverage in terms of generalisations, such a qualitative study offer a deeper look into the identity and federal politics, which illuminate the dynamics at the heart of most multinational federations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".