Bibliographic record
Abstract
absolute competition 135 active population 246 advanced corporate services 252 aerospace 48 age dependency 232, 234 age-related programs 232 agglomeration economies 249, 252, 254, 259, 263 aging population 1, 19, 232 agricultural commodities 272 Airbus 203 Aker Yards 203 Alliance NumériQC 56 Americans for the Arts 237, 244 anchor institutions 26, 33 area vasta 108 Asian fi nancial crisis 133 Association des producteurs en multimédia du Québec 60 Association for Community and Higher Education Partnerships 33 associations 126 Atlanta 237 Aurora 215, 217 Avenue of the Arts 240 Baosteel 208, 220 Bari 99 Barletta 98 Bartlett, Randall 2 Bassanini laws 91 Bayer 203, 214, 217 benchmarking 16 benefi ts of industrial tourism 220 bio-tech parks 250 biotechnology cluster 86 Brookings Institution 3 Bureau of the Census 233 business environment 200 business park, Knapsack 224 business sociology 125 business tourists 223 Carbonaro, G. 75 Cassidy, E. 39 categories of cities and towns 237 central places 263 central urban density 269 centrality 269 centralized control 267 Centre d'Expertise et de Services Application Multimédia 60 Charlottesville 237 chemical business park 203 Cheshire, Paul 75 Chicago 20, 278, 279 China Development Institute 149 Chinese Academy of Social Sciences 16 Chinese competition 106 Cité du multimédia 57, 63 cities in Northeastern US 16-17, 181 CLUNET (Cluster Network) 49 clusterization 53 clusters 38, 44 CMM economic development plan 48 Coalition for Urban Serving Universities 32 codifi ed knowledge 74 Cointreau 217 college or university towns 18, 238, 241 colleges and universities 243 Cologne 203, 205, 207, 210, 214, 217, 219, 222, 224 commercial opening 117, 124 company visits 202 competition and cooperation 201 competitive performance of nations 121 competitiveness 74, 94, 121, 128, 153, 178, 248, 256 concertation 94, 99 connectivity 81 consumers of cultural activities 236 coordinator 163 core dynamics, 255
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.808 | 0.693 |
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".