From the ‘Culture Wars’ to the Conservative Campaign for Campus Diversity: Or, How Inclusion Became the New Exclusion
Bibliographic record
Abstract
This article explores the new conservative assault on the university and the relative silence on the part of progressives in response to this challenge. In part, this apparent retreat is a consequence of the vulnerabilities and anxieties of workers in the academy that result from the ongoing corporatization of the university as well as the pervasive culture of fear that permeates the USA in the wake of 9/11, which tends to punish critique as anti-American. As important as such factors are, the current analysis focuses more inwardly on processes of internalization and normalization of the tenets of professionalism and (neo)liberalism in the post-civil rights American academy. Upon careful reexamination of the ‘culture wars’ of the 1980s and 1990s, it locates part of an explanation for such confounding quiet in the ideals that marked the university's ‘multicultural turn.’ The often limp endorsement and bland acceptance of principles such as ‘nondiscrimination,’ ‘diversity,’ and ‘openness’ in the abstract enabled the Right's ruthless appropriation of the vision and language of civil rights, turning fact and history on their heads.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.069 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".