Bibliographic record
Abstract
Over the last years many policy-makers and analysts have put effort on measuring and comparing regional competitive performance. This goes back to the fact that the concept of competitiveness was taken over from the business field and applied on the national and regional level. And as competitiveness is a relative concept, it implies the need to compare with others such that regions are inexorably sucked into the continual monitoring and periodic benchmarking of what ?the competition¡® is doing and where the ?best practice¡® or ?best offer¡® lies. Therefore, efforts have increasingly focused on the development of composite indices which combine relevant indicators into one overarching measure, the results of which can be reported in the form of a ?league table¡®. Such indices and rankings attract widespread attention in the media and could be regarded as a potentially useful means of helping firms, policy-makers and institutions to assess the performance of their economies in comparable (i.e. numerical) terms, and to undertake appropriate remedial strategies. This paper gives an overview on some of the indices to be found in the world, analyzing them with respect to indicators included and predictive quality. We conclude with some reflections on the value and role of measures of regional competitiveness.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".