Can the University Escape From the Labyrinth of Technology? Part 3: A Strategy for Transforming the Professions
Bibliographic record
Abstract
This third part continues the exploration of how we can overcome the limitations of the present knowledge system. In preparation, two aspects of current engineering theory and practice are examined because they are paradigmatic: the concept that engineering is essentially problem-solving, which goes against our understanding of human skill acquisition, and the existence of parallel modes of knowing technology derived from professional education and practice and from living in a society permeated by technology. The former suspends practitioners in the previously examined triple abstraction as a primary microlevel characteristic of the current knowledge system and is thus separated from experience and culture. The second mode of knowing derives from daily life experience, symbolically represented by the organization of the brain-mind, which also functions as a mental map for skillfully coping with the world according to a culture. The practitioner primarily derives analytical exemplars from the former knowing and design exemplars from the latter. It is shown how this affects the building of cities and the design of production systems. Umbrella concepts and round tables are introduced as further steps toward transforming university departments, and (as we will see in Part 4) the operation of the university.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".