Can the University Escape From the Labyrinth of Technology? Part 1: Rethinking the Intellectual and Professional Division of Labor and its Knowledge Infrastructure
Bibliographic record
Abstract
The role tradition played in preindustrial societies has been supplanted by the decisions of countless specialists organized by means of an intellectual and professional division of labor shaping a knowledge infrastructure that sustains these decisions. Three limitations of this knowledge system are discussed: (a) on the macrolevel, it imposes an end-of-pipe approach for dealing with the undesired consequences of decision making, rarely getting to the root of any problem; (b) on the microlevel, individual practitioners of a specialty are trapped in a triple abstraction, leading to a poor ratio of desired to undesired effects of their decision making; and (c) on the intermediate level, it bars the road to genuine solutions to many difficulties faced by contemporary civilization. In this first of four articles, the beginning of a response is developed for the profession of engineering, which will be paradigmatic for other professions, the social sciences, and the university as a whole.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| 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".