Bibliographic record
Abstract
While some may want to argue the point, many believe it is innovation and the resulting technology that provides society with the impetus to advance and to provide the greatest value to the members of that social order. Some would also argue that advanced or advancing technologies provide the fastest way to improve the health, wealth, and well being of the individual. Independent of these arguments, it is clear that innovation, particularly the game-changers, has had an accelerating impact on the development of almost every social order on this planet. It is through the creativeness of the individual, plus the organized efforts of research and development programs that have allowed the fostering of ever-growing numbers of new innovations in every aspect of society: agriculture, medicine, transportation, communication, etc. What may be of particular note is that some of the earlier and most contributive to the innovation race are currently less than effective than they once were, or possibly others are simply out-running them. The United States, plus several others, was one of the earlier contributors to the technology revolution. By most of the standard global measures it is clear the US has not maintained the edge in technology and innovation that was, for many decades, the beacon to a large portion of the rest of the world. While the US is not the only country that has allowed the innovation gap to slip and in some cases to reverse, it may be very representative of the reason the rest have also slowed their progress. More importantly, the reasons may have very little to do with capabilities, resources, education, manpower, etc. It may simply be managed expectations, thus the purpose of this paper.
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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.174 | 0.137 |
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".