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
Abstract Across the world, cities and regions have wasted trillions of dollars blindly copying the Silicon Valley model of growth creation. We have lived with this system for decades, and the result is clear: a small number of regions and cities are at the top of the high-tech industry, but many more are fighting a losing battle to retain economic dynamism. But, as this books details, there are other models for innovation-based growth that don’t rely on a flourishing high-tech industry. Breznitz argues that the purveyors of the dominant ideas on innovation have a feeble understanding of the big picture on global production and innovation. They conflate innovation with invention and suffer from techno-fetishism. In their devotion to start-ups, they refuse to admit that the real obstacle to growth for most cities is the overwhelming power of the real hubs, which siphon up vast amounts of talent and money. Communities waste time, money, and energy pursuing this road to nowhere. Instead, Breznitz proposes that communities focus on where they fit within the four stages in the global production process. Success lies in understanding the changed structure of the global system of production and then using those insights to enable communities to recognize their own advantages, which in turn allows to them to foster surprising forms of specialized innovation. All localities have certain advantages relative to at least one stage of the global production process, and the trick is in recognizing it.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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