The Occupation—Industry Mismatch: New Trajectories for Regional Cluster Analysis and Economic Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it