UniverCity: The Vicious Cycle of Studentification in a Peripheral City
Bibliographic record
Abstract
Research on studentification has unpacked the spatial, economic, and social impacts that are associated with the growing presence of students in cities. Nonetheless, considerably less attention has been paid to the broader regional and national contexts that shape studentification. Using the case study of Ben–Gurion University of the Negev, Beersheba, we argue that the studentification of the city should be understood within its context as the periphery of the country. Despite the university's central location and its involvement in revitalization efforts in the region, Ben–Gurion University is surrounded by marginalized neighborhoods which have turned into a “student bubble”. We show that the segregation between the campus and the city results from a vicious cycle that reproduces the city's poor image and disrupts the university's attempts to advance the city and region. Although overlooked by policy–makers, the implications of this cycle reach far beyond the campus' surrounding and affect the city and to some extent the whole region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".