Bibliographic record
Abstract
Abstract Why do some societies embrace innovative technologies, policies, and ideas, while others are slow to adopt, and some even resist, them? Incumbent producers are most likely to be affected by certain kinds of innovations; they also wield a disproportionate influence in the design of institutions and policies that encourage or limit their adoption. We show formally that the elite has four cardinal policy options: to appropriate the innovation for itself; to encourage its adoption; to tax, regulate, or limit the innovation; or to block it. We show that six features of an innovation determine how it is received: (i) whether it is easy to replicate; (ii) whether it complements or competes with the elite's sources of income; (iii) whether its impact is broad or narrow; (iv) whether it is location‐dependent, and (v) concealable; (vi) whether it requires large fixed costs. While other works have occasionally considered one of these factors, we show where each feature comes from, and we assess them systematically and together. We provide illustrative evidence of the relevance and generality of the model to understand the fate of a variety of innovations.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".