Can the University Escape From the Labyrinth of Technology? Part 2: Intellectual Map-Making and the Tension Between Breadth and Depth
Bibliographic record
Abstract
This second part continues the search for ways of overcoming the three limitations of the current intellectual and professional division of labor and its knowledge infrastructure, which were shown to be at the root of the present economic, social and environmental crises. A complementary knowledge strategy is proposed to counterbalance the trade of breadth for depth, based on the creation of intellectual maps. One such map is described for engineering, showing how through the process of industrialization people change technology and how through its influence on human life and society, technology changes people. Because industrialization cannot destroy the matter and energy it requires, it also transforms its relations with the biosphere. Once the connections between technology and everything else are mapped, specialists can inquire into the consequences of their design and decision making that fall beyond their domains of expertise, to introduce a preventive orientation into their work to achieve a better ratio of desired to undesired effects. This is shown for materials and production, energy, work, and cities. In subsequent parts, it will become apparent that this example is paradigmatic for other professions, the social sciences, and the university.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".