How Universities Have Betrayed Reason and Humanity—And What's to Be Done About It
Bibliographic record
Abstract
In 1984 the author published From Knowledge to Wisdom, a book that argues that a revolution in academia is urgently needed, so that problems of living, including global problems, are put at the heart of the enterprise, and the basic aim becomes to seek and promote wisdom, and not just acquire knowledge. Every discipline and aspect of academia needs to change, and the whole way in which academia is related to the rest of the social world. Universities devoted to the pursuit of knowledge and technological know-how betray reason and, as a result, betray humanity. As a result of becoming more intellectually rigorous, academic inquiry becomes of far greater benefit to humanity. If the revolution argued for all those years ago had been taken up and put into academic practice, we might now live in a much more hopeful world than the one that confronts us. Humanity might have begun to learn how to solve global problems; the Amazon rain forests might not face destruction; we might not be faced with mass extinction of species; Brexit might not have been voted for in the UK in 2016, and Trump might not have been elected President in the USA. An account is given of work published by the author during the years 1972–2021 that expounds and develops the argument. The conclusion is that we urgently need to create a high-profile campaign devoted to transforming universities in the way required so that humanity may learn how to make social progress toward a better, wiser, more civilized, enlightened world.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.037 | 0.045 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".