Being Woke to Anti-Intellectualism: Indigenous Resistance and Futures
Bibliographic record
Abstract
Introduction Anti- intellectualism (the opposition or hostility to intellectuals or to intellectual pursuits) is a practice and a stance that continues to plague so- called Australian society. During the years of John Howard's Liberal conservative government (1996– 2007), the so- called ‘history wars’ reflected and defined such anti- intellectualism (Manne, 2009). This stance against Indigenous struggles used oppressive ideologies and sought to strengthen a colonial settler mindset via education and public discourse. The history wars intensified and legitimated the underlying racism that has long structured the national, bringing it openly into mainstream discourse once more (Attwood and Markus, 2007). For scholars of Indigenous studies in Australia, this anti- intellectual project poses significant impediments. Mainstream and social media re- readings or misrepresentations of identity politics have picked apart, debated and interrogated our cultural and other identities (Carlson, 2016). This turning point has had a significant regressive impact on anti- racist and decolonial literacies in all spaces of national public engagement. Despite changes in government, anti- intellectualism and the accompanying racism have not subsided, but rather lent themselves to the co- optation, distortion and redeployment by conservatives of terms originating from the left, such as ‘political correctness’ or ‘PC’ and, attacks on critical race theory (CRT). Most recently, they have been encapsulated by the populist mantra of ‘anti- woke’ culture. The anti- intellectualism of these projects works to distract from and dismiss collective movements that mobilize around demands for change. Traced to Black American blues singer Huddie William Ledbetter – known as Lead Belly – the term ‘woke’ appears in a brief discussion that follows their Smithsonian recording of the song ‘Scottsboro Boys’ in 1938, which tells the story of nine Black teenagers accused of sexually assaulting two white women (Lead Belly, 1936). The warning to ‘be careful … stay woke’ is a clear directive to Black people regarding the dangers of white supremacy. The onset of anti- woke culture – that is, opposition to anything perceived as ‘woke’ – has spread rapidly across campuses and public spaces in the United States, Australia, Canada and Europe (Rhodes, 2021 ; Pilkington, 2021).
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".