Bibliographic record
Abstract
agriculture of Poland no collectivization under USSR 81 Alibaba, Internet-based businesses B2B online marketplaces 185 analysis of cross-impact results, China 177 anchors and processes, interaction among 47, 145 anchors grouped into major clusters 159-60 Germany 100 Shanghai ZJ Park, China 181 Anti-Concentration Act, Israel 60-61 Apple, China 187-8 Archimedes, shouting "Eureka" 22 Asian learning, Confucian 22 attractiveness indicators for Canada 142 Avanza I (2005-09) 130 Avanza II (2009-2015) development of ICT sector 130 ICT training for firms, employees, elderly people 131 B2B (business-to-business) 185 bank finance in China 183 Bartlett's sphericity test 177 "basic infrastructure", Israel's low ranking 62 Beijing Genomics Institute (BGI) genetic sequencing company 187 Berlin, potential site for innovative entrepreneurs 103 best-practice benchmarking management tool 224-5 Biomedical Sciences Sector (BMS) Singapore pharmaceuticals, biotechnology, medical devices 206 Biopolis, Singapore biomedical sciences city 207 biotechnology in Israel 58 Build Your Dreams (BYD) up-start car company 186-7 bureaucracy and regulation, stifling in France 115 business, ease of, in Poland 68-9 Business Acceleration Program (BAP) Ontario 153 cake printing method, Italy
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.376 | 0.259 |
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".