Beyond populist politics: why conventional politics needs to conjure myths of its own and why it struggles to do so
Bibliographic record
Abstract
The last ten years has seen the rise of populist forces across the globe from both the right and the left. While often read in the context of the perceived rise of, and reactions to populist and potentially violent Islam, this analysis is excessively focused on observations of the right, and ignores similarities between the populist right and the populist left. By way of an alternative, this article draws together political theology, original ethnography and observations of contemporary politics in the United Kingdom (and to a lesser extent the United States and Canada) to offer a broader lens involving: the rise of liberalism, the consequent construction of politics as technocratic management, and the neglect and resultant disillusionment of ordinary people. In particular, the paper draws on my recent research on the role of myths of solidarity in developing civic engagement. It argues that myths of solidarity have been undermined by the rise of liberalism, and that restoring such myths to the center of contemporary politics is vital to challenging the myths of division that fuel populist politics. Finally, it explores possibilities for restoring myths to the center of contemporary politics, suggesting that while avenues for such reform are emerging, politics as technocratic management remains the dominant paradigm.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.151 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".