Enacting and rearticulating “accountability”: reactionary watchdogs and the staging of anti-woke culture wars
Bibliographic record
Abstract
A commitment to the principle of political accountability is part of the common sense of liberal democratic societies that is particularly important to journalists. It shapes the adversarial posture of a fourth estate narrative where journalists act as watchdog critics “holding power to account” on behalf of the public. This paper examines how the political meaning of accountability talk is “conceptually flipped” in an atmosphere of culture war conflicts where the very idea of critique takes on a reactionary valence. I introduce the concept of the reactionary watchdog – my shorthand for capturing forms of accountability work and (quasi)journalistic practice that normalize a horrific far-right representation of left-wing identities. The notion of reactionary watchdogism blurs any neat distinction between “liberal” and “illiberal” politics and resonates with sedimented institutional logics that are part of the political legacy of neoliberalism. I support the conceptual argument by discussing the case of Christopher Rufo, the US “anti-woke” activist who first came to national prominence by spearheading the attack against critical race theory. I focus on two podcast interviews Rufo did with another exemplary culture war figure, the Canadian psychologist Jordan Peterson.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".