Neoliberalism and the Attack on Education: An Interview with Henry A. Giroux
Bibliographic record
Abstract
In your latest book, Neoliberalism's War on Higher Education, you identify the broad project of neoliberalism as the key mode of governance, policy and ideology threatening public education.What are your greatest concerns?How can we fight back?Henry A. Giroux 2 (HAG): Public and higher education are under siege in neoliberal societies for a number of reasons.First, they are under attack because they are public, not necessarily because they are failing.As public sites, they offer potential spaces, pedagogies, and modes of thinking that are critical, thoughtful, and, frankly, dangerous because they not only offer the conditions to inspire students to be self-reflective and critically engaged, but also energize them to connect what they learn to what it might mean to hold power accountable, address social injustices, and both imagine and struggle for a more just world.Public and higher education are considered dangerous because they harbor the possibility of speaking the unspeakable, uttering critical thoughts, producing dissent, and creating students willing to hold power accountable.The apostles of neoliberalism, on the other hand, want to eliminate the critical function of public education on all levels and they are working hard to transform these institutions into disimagination factories.My biggest fear that as more and more students labour under onerous debt, subjected to a form of indentured citizenship, coupled with modes of 1
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".