Bibliographic record
Abstract
Tables 1.1 Innovation efficiency 13 3.1 List of Israeli innovation anchors 48 3.2 List of identified processes fostering Israeli innovation, ranked by importance and classified by market side (supply, demand, or both) 49 3.3 Factor analysis results for the Israeli innovation ecosystem 52 4.1 List of Polish innovation anchors 71 4.2 List of identified processes fostering Polish innovation, ranked by importance and classified by market side (supply, demand, or both) 72 4.3 Factor analysis results for the Polish innovation ecosystem 74 4.4 List of anchors grouped into major clusters 76 5.1 List of German innovation anchors 90 5.2 List of identified processes fostering German innovation, ranked by importance and classified by market side (supply, demand, or both) 91 5.3 Factor analysis results for the German innovation ecosystem 93 5.4 List of anchors grouped into major clusters 100 6.1 List of French innovation anchors 111 6.2 List of identified processes fostering French innovation, ranked by importance and classified by market side (supply, demand, or both) 112 6.3 Clusters (group of anchors) 113 6.4 Factors (group of processes) 113 7.1 List of Spanish innovation anchors 126 7.2 List of identified processes fostering Spanish innovation, ranked by importance and classified by market side (supply, demand, or both) 127 7.3 Factor analysis results for the Spanish innovation ecosystem 129 7.4 Comparison of programs by main activities and resources 134 7.5 List of anchors grouped into major clusters 135 8.1 Percentage change in GDP, 2010-2017, Canada v USA 140 8.2 List of Province of Ontario innovation anchors 146
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.833 | 0.673 |
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".