Robert Weissman Multinational Monitor
Bibliographic record
Abstract
The rapid commercialization of applied genetics in the mtd-1970s, accompanied by a sudden rise in academic-corporate partnerships, raised questions about the impacts these linkages have had on the social and professional norms of scientists. The extent and pattern of faculty tnvolvement in commercialization of biological research is largely an unexplored area. This article provcdes a quantitative assessment of the linkages between biology faculty in American uncverscties and the newly formed biotechnology industry. The results of thes study, covering the period 1985-88, show that academic scientists responded en masse to participating in the commercialization of genetecs research by estabhshmg formal associations with many of the new biotechnology compances. A data base consisting of 889 U.S. and Canadian biotechnology companies and 832 sccentcsts who had formal ties to them was developed over a four-year period. The patterns of academic-corporate Icnkages are revealed by institution. Three univer-sities with the most commercially active faculty are Harvard, Stanford, and MIT. Of the 359 bcomedeca! scientists and geneteccsts who were members of the Nateonal Academy of Sceences (en 1988), at minimum, 37 % had formal ties with the biotechnology industry. Rapid commercialization of the biological sciences began several years after the 1973 discovery of recombinant DNA (rDNA) molecule techniques. Potential applications of &dquo;gene splicing&dquo; to a wide range of industrial, agricultural, and pharmaceutical products stimulated the founding of hun-dreds of new firms (see Figure 1). Billions of dollars of venture capital were invested in just a few years (U.S. Congress, Office of Technology Assessment
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.060 |
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".