Bibliographic record
Abstract
Not long ago, under the influence of Michel Foucault, one spoke of the conjunction of knowledge and power, but in this post-truth era power appears singularly uninterested in knowledge, even as the supporters of Donald Trump claim that he alone of all politicians speaks the truth. This essay proposes to examine the relations of power and knowledge under the present populist assault. This analysis begins in the work of Claude Lefort, who spoke of the separation of knowledge and power in democracy’s symbolic regime, and is then counterposed to Ernesto Laclau’s understanding of ‘populist reason’ in order to explore the present torsion of this relation to the point where power can appear not just separated from, but opposed to knowledge. It will be argued that it is less a question of post-truth than of different forms of truth with different truth claims, borne by different imperatives, and tied to different forms of representation – truth claims that can, in relation to each other, be indifferent, complementary, or conflictual. With this in mind, the essay asks: what is the relation of the people to truth? Do those who claim to represent the people seek possession of a different kind of truth? What is the relation of populism to ideology? And what is populism’s relation to ‘post-modernism’?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.092 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".