Bibliographic record
Abstract
The R&D game is played in different competitive contexts. A study of sixty–nine firms led to the identification of four contexts: (i) technology races, (ii) learning in technological systems, (iii) technical parity competition, and (iv) market contests. For each type of competitive context, different forms of integration of R&D into the business have been observed: (i) R&D at the science–frontier, (ii) revenue–dependency, (iii) cross–functional integration, and (iv) strategic arena R&D. The quality movement has found successive applications in manufacturing, marketing and new product engineering. Recently, the requirement by many clients that suppliers be certified by third parties and the need to improve the performance of R&D raises the question of the applicability of quality approaches to R&D. Our argument is that the quality movement is applicable to R&D as it brings a new cognitive mindset to the concern of managing R&D effectively. Firms stand to gain enormously from R&D functions that operate with high levels of awareness and strategic orientation.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".