Can the University Escape From the Labyrinth of Technology? Part 4: Extending the Strategy to Medicine, the Social Sciences, and the University
Bibliographic record
Abstract
This fourth part outlines a strategy for overcoming the limitations of the knowledge system for engineering by combining intellectual maps, preventive approaches, umbrella concepts, and round tables as described in the earlier parts. A discussion of the issues faced by modern medicine illustrates the paradigmatic nature of the diagnosis and prescription made for engineering. The social sciences face mirror-image problems. One response has been the rise of new disciplines such as communications, environmental studies, urban affairs, criminology, and policy studies. To avoid the limitations of discipline-based knowing and doing, a similar strategy for their transformation will have to be implemented. Considerable synergies would result if parallel efforts to transform the present knowledge system were carried out throughout the university. Some suggestions are made as to how this can be supported by organizational and institutional changes. Finally, it is suggested that such a transformation of the university could make a critical and decisive contribution to overcoming the current economic, social, and environmental crises.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".