WHERE DO GAMES OF INNOVATION COME FROM? EXPLAINING THE PERSISTENCE OF DYNAMIC INNOVATION PATTERNS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper contributes to explaining how and why distinct games of innovation emerge by suggesting that games are nested in innovation systems with persistent innovation dynamics. Dominant lifecycle models focus on how innovation systems transit from an effervescent stage, to product innovation, to process innovation, and so on. They propose specific mechanisms and limiting conditions that affect knowledge production and investment to explain these systematic transitions. Building on these models, we rethink the conditions and mechanisms of innovation to suggest that endogenous renewal cycles can re-create the knowledge and funding necessary to maintain innovation systems for long periods in one stage. We take steps towards developing a theoretical model of innovation dynamics that extends the applicability of lifecycle theories and unifies them with emerging views such as high-velocity innovation and hyper-competition. We also describe three possible types of endogenous renewal cycles, each sustaining a different level of knowledge dynamism and enabling different types of games 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it