Bibliographic record
Abstract
Much has been written about the linear model of innovation. While it may have been the dominant model used to explain technological innovation for decades, alternatives did exist. One such alternative—generally discussed as being the exact opposite of the linear model—is the demand-pull model. Beginning in the 1960s, people from different disciplines started looking at technological innovation from a demand rather than a supply perspective. The theory was that technological innovation is stimulated by market demand rather than by scientific discoveries. However, few traces of the demand-pull model remain in the literature today. This article looks at what happened to the demand-pull model from a historical perspective, at three points in time: birth, crystallization, and death. It suggests that the idea of demand as a factor explaining technological innovation emerged in the 1960s, was formalized into models in the 1970-1980s, then got integrated into “multidimensional” models. From then on, the demand-pull model disappeared from the literature, existing only as an object of the past, like the linear model of 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".