Bibliographic record
Abstract
Political actors across the globe often use the language of democracy, but they do not all use the same language. Drawing on content analysis of 1935 speeches given between 2000 and 2010, this study examines how five North African autocrats appropriated the global discursive form of democracy by altering its content. These leaders proposed that the special circumstances of each country preclude any one-size-fits-all global definition of democracy, whose imposition in their countries, they claim, would be inappropriate, ineffective, or dangerous. Through their speeches, these rulers redefined democracy by engaging in active ideological work, weaving together discourses that combined global norms, state interests, and local values. This suggests that, in addition to being a benchmark by which to measure modes of governance, ‘democracy’ is also a language game played between actors on a global stage. By synthesizing theoretical frameworks drawn from world polity and social movement studies traditions, this study shows that peripheral actors may adapt global discourses purposefully and strategically rather than encountering them as passive participants in a purely mimetic cultural diffusion process. This has implications for a wide range of global norms that are open to appropriation by local actors drawing on domestic and external political developments and experiences.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".