Bibliographic record
Abstract
Tables 2.1 Aggregate assessment of 18 origins by 15 samples 29 2.2 Well-known versus less well-known product origins 32 2.3 Nodal sectors in the images of the U.S. and Japan 34 2.4 Top-of-mind awareness for fi ve main technology cluster regions in North America 36 2.5 Summary ISM for Canada, Australia, and Japan 37 2.6 Use of 'place' in U.S. and Canadian magazine advertisements 39 10.1 Three overlapping stages in the development of 'competitiveness' as hegemonic policy discourses since the 1960s 167 10.2 Examples of institutions and discourses related to competitiveness at diff erent scales 171 10.3 Two knowledge apparatuses and knowledging technologies in the construction of 'competitiveness' 172 10.4 World Economic Forum and its global competitiveness rankings of the USA and selected Asian countries, 2004-08 174 10.5 Institutions and practices in building capacities and organizing themed clusters in Asia 176 10.6 Technology of agency that organizes regional spaces, policies and populations 177 12.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.860 | 0.678 |
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".