Improving the Traditional Measures of Agglomeration with Neighbouring Effects
Bibliographic record
Abstract
The traditional measures of concentration are based on the hypothesis that agglomeration forces among plants appear only inside of a single area and they do not have any effect outside of it. Instead, it is reasonable thinking to the existence of factors exerting their effects not only within the territory where they were originated but, in the meantime, producing spillovers into neighbouring areas. Building on this assumption, this paper contributes to the agglomeration literature by proposing an extension of the traditional agglomeration measures that account for externalities that cross the barriers of the geographical units under analysis. So, we want to examine the spatial dependence among agglomerative forces in contiguous areas. In order to measure the spatial agglomeration we used the index proposed by Maurel and Sédillot, but in the recent version by Maré and Timmins, after inverting the role of the industry with that one of the area to obtain an agglomeration measure for each Local Labour System. In particular, we assume that urbanization externalities cross the borders of the single local reality falling down on the adjacent ones or whatever nearby.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".