When Your Neighbourhood Changes: Living Through Gentrification in Amsterdam Oud-West
Bibliographic record
Abstract
This qualitative study investigates the lived experiences of gentrification for locals in the urban neighbourhood of Oud-West in Amsterdam. A gentrification policy was used to turn this neighbourhood with a relatively low socioeconomic status and limited property investment into an attractive area of reinvestment and economic activity. For locals, this strategy resulted in changes to the urban landscape, such as soaring housing prices, new investment projects, tourism, and a new, transient, young urban professional group of inhabitants. Following this demographic change, the locals that have not been physically displaced nevertheless experience a sense of displacement. By analyzing the concept of ‘transience’, this study shows how the relatively short and less integrated stay of global young urban professionals results in a perceived loss of social cohesion. Moreover, this young urban professional population’s increasing demand for an ‘Airspace’ type of hospitality radically changes local and authentic businesses, resulting in a perceived lack of diversity and authenticity. Furthermore, locals report how they experience new inhabitants to be less tolerant towards ‘big city life’, and have a stronger sense of malleability.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".