Navigating Between National Unity and Political Expediency: The Federal Government’s Response to Substate Identity Policy in Nigeria and Canada
Bibliographic record
Abstract
What factors shape the federal or national governments’ responses to substate identity policies within deeply diverse federal systems? This article answers this question by analysing the role of the President or Prime Minister as ‘bearers’ of national unity, when confronted with (exclusivist) identity policies that may threaten the inclusion of all citizens, in particular minorities residing in jurisdictions dominated by a dominant (ethnocultural, linguistic or religious) group. The empirical evidence draws on the experiences of Canada and Nigeria, focusing in particular on the tension over secularism and the Muslim veil in Québec public institutions, and the adoption of Sharia by twelve Muslim-majority states in Nigeria. The paper demonstrates that while the need to uphold cultural diversity could explain the federal government’s responses to the identity policies implemented in these regions, the primary driver of the responses was the president and prime minister’s political goal of re-election and retaining power. The paper contributes to the literature by stressing the similarities in the political reflexes and strategies utilized by political actors in managing the tension between the constitutional principles of self-rule and shared rule in two federations that are often thought to be so different and, hence, incomparable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".