Bibliographic record
Abstract
absence, leave of absence schemes (N) 73 academic spin-offs (ASOs) 5, 6, 28, 103, 142-53 Belgian case study 143 knowhow/skills 142-53, 157-68 team member entry 142-53 venture capital acquisition 157-68 see also universities accounting knowhow 162 adoption early 30 rate 30 advice 7 age factors 17-18, 47, 50, 52, 54, 161-5 agency 175 agriculture 42, 50, 52, 54, 56 angels, business see funding anti-trust 33 Asia 95 see also Singapore; UK, ethnic work force; USA, ethnic workforce ASOs (academic spin-offs) see universities bank loans immigrants, access to 122 bankruptcy 30, 33 barriers to entry 15 to uptake of commercialization of research (universities) 61 Bayh-Dole Act see USA, legislation Beiarn jazz festival see jazz Belgian ASO/team development case study 143 biological model 27 see also science; technology biotech industry 161, 163 Boards (ASOs) 6, 143-53, 162-8 see also education; knowhow; management; skills Boston see USA 'boundary' see Katz and Gartner Framework business angels see funding development 101 plan 51 services sector 50, 52, 54 Canada 75 'cognitive diversity' 103, 144, 158, 161-6 see also education; knowhow; skills communications problems 179 sector 52 team 179, 180 'community' defined 81 see also CVs companies disclosure and reporting requirements 33 lifestyle firms 188 limited liability 193 'mother companies' 95-8, 102 SMEs 188, 189 sole traders 193 competition law see anti-trust conflict 5, 6, 7, 128-37 'affective' 130, 131
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.663 | 0.503 |
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".