Bibliographic record
Abstract
The thesis is composed of four chapters, which investigate different topics in the field of applied urban and spatial economics. The first paper develops an original empirical approach to investigate the role played by labour markets in explaining the pattern of industrial agglomeration in the United States. The methodology allows us to i) obtain an estimate of industrial agglomeration which significantly improves on existing indices, and ii) provide a ranking of industries according to their responsiveness to labour market determinants of agglomeration. Results show that labour market determinants explain around one quarter of the variation in spatial agglomeration across industries. The second paper assesses whether urbanization alleviates rural poverty in surrounding areas in India, using a panel dataset at district level for the period 1981-1999. We find that the effect is substantial and systematic; this is largely attributable to positive spillovers from urbanisation, rather than to the movement of the rural poor to urban areas per se. The third paper investigates an extremely peculiar characteristic of the US patent dataset: there is a large group of inventors who develop one or a few patents during a long period of analysis ("comets"), while a very small group of "stars" inventors develop a huge number of patents. In light of that, the paper first explores the location pattern of comets and stars, and then assesses whether the activity of star inventors is beneficial to the production of comet patents in the same city and technological category. The fourth paper describes the effects of bank liberalization on the geographical penetration of branches in the city of Antwerp (BE). Our results show that, coinciding with the strongest wave of the deregulation and concentration process, banks systematically exit from low income neighbourhoods.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".