Bibliographic record
Abstract
Many research-intensive universities have moved into the business of promoting technology development that promises revenue, impact, and legitimacy. While the scholarship on academic capitalism has documented the general dynamics of this institutional shift, we know less about the ground-level challenges of research priority and scientific problem choice. This paper unites the practice tradition in science and technology studies with an organizational analysis of decision-making to compare how two university artificial intelligence labs manage ambiguities at the edge of scientific knowledge. One lab focuses on garnering funding through commercialization schemes, while the other is oriented to federal science agencies. The ethnographic comparison identifies the mechanisms through which an industry-oriented lab can be highly adventurous yet produce a research program that is thin and erratic due to a priority placed on commercialization. However, the comparison does not yield an implicit nostalgia for federalized science; it reveals the mechanisms through which agency-oriented labs can pursue a thick and consistent research portfolio but in a strikingly myopic fashion.
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 | 0.056 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".