Bibliographic record
Abstract
Background The usefulness of the information derived from a survey depends on the wording of the questions that are asked, the information guides that are provided to the respondents, and the professionalism of the interview teams. In Chapter 2, we provide a general outline of the nature of the survey and its operations. Here we list the set of questions that were sent out to the sample of companies included in the survey and a brief outline of some of the definitions that were provided as a guide to the respondents. A successful survey requires the development of questions that respondents willingly answer, that are clear, and that do not pose an onerous response burden. For that reason, preproduction tests were conducted with potential respondents. These meetings taught us that companies were willing to talk about their major innovations because they took pride in them, especially individuals who were closely associated with the innovation process. The title of the individual who could answer detailed questions about innovation varied firm by firm. In some cases, this would be the R&D manager; in other cases, it would be the vice president in charge of production engineering, or a vice president in charge of new product development. But after we had outlined the nature of the survey, our main company contact was able to direct us to the individual in the firm who could answer our questions about the innovation process at that particular firm — and these individuals had detailed firsthand knowledge about product and process innovation.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".