The Occupation—Industry Mismatch: New Trajectories for Regional Cluster Analysis and Economic Development
Bibliographic record
Abstract
This article is a natural extension of the current discussion on occupational clustering and economic growth. It is argued that, while there has been increased interest in the role of occupations, little has been done from a methodological and empirical approach to discover how the study of occupations can illuminate the study of industry. Prior work in cluster analysis has generally taken an ‘either/or’ approach towards occupational and industrial analysis. Porter’s clustering model has illuminated the cross-fertilising linkages across industries, but this is only half the story. It is argued that what drives these clusters is not only the industry, but also the people and their occupational skills and, therefore, such analysis must be expanded. Using the case of the IT sector in Los Angeles, the industry approach is combined with an ‘occupational cluster analysis’. It is concluded that this approach leads to a better understanding of regional competitiveness and growth.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".