BOOK REVIEW - PHILIP MCCANN (EDITOR), INDUSTRIAL LOCATION ECONOMICS
Bibliographic record
Abstract
The book Industrial Location Economics published by Edward Elgar Publishing Limited is a highly relevant example in this respect. The Editor, Professor Philip McCann from the University of Reading, UK, a brilliant representative of the new generation of regional scientists, has succeeded in attracting around this subject internationally renowned scholars belonging to top universities form Austria, the Netherlands, UK, Italy, Canada, USA and Japan. They address a variety of topics concentrating on the spatial behaviour of individual firms and the growth and generation of industrial clusters and cities, offering an original, comparison and contrast-based combination of classical and more recent approaches to location analysis. A wide range of analytical techniques is employed – some of them being the authors’ contribution to enriching the investigation methodology of industrial location, that has made it possible to address the same issues from exciting, original perspectives. The excellent combination between theoretical knowledge and empirical testing, between traditional and modern approaches to industrial location economics as well as the clarity of the ideas expressed recommend this worthwhile book to both academics and students in geography, economics, management as well as to experts in institutions involved in regional and urban planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.043 |
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".