Bibliographic record
Abstract
Neighbourhoods can be seen as units that have a function in certain phases of the lives of people. The ideal seems to be that they more or less ‘match’ the household type. Because of frequent changes in the household and also because of changes or inertia in several neighbourhoods mismatches may develop and therefore, when able, households will frequently try to ‘rematch’ their relationship with the neighbourhood. If we assume that the housing market is functioning reasonably well, a cross-sectional view on where household types have settled down will reveal relevant information about the relationship between household types and neighbourhoods. In this contribution we investigate that relationship in great detail. We apply a large dataset with individual level register data for the whole population of the metropolitan region of Amsterdam from which we can construct class fractions, which form the basis for explaining different neighbourhood orientations. The class fractions are constructed with information of the precise economic sector people are working in combined with – individual level – information on disposable income. We focus on low, middle and high income individuals, who are employed within fifteen contrasting employment sectors and for which we can analyse their neighbourhood orientation. For that purpose we constructed a large number of different neighbourhood types in the urban region of Amsterdam. The types were based on whether: 1) the location is in the urban core or not; 2) the population density of the neighbourhood and the size of the municipality; 3) housing real estate values in the neighbourhood; 4) and whether the neighbourhood consists of housing which is predominantly pre-war or not. While the relationship between class fractions and neighbourhood types is central to our investigations, we controlled for other obvious factors that impact upon residential orientations, such as age, family type, gender, and country of origin. We show that creative cultural class fractions are strongly overrepresented in the most urban milieus, more precisely Amsterdam milieus with middle status or high status. The most overrepresented class fractions in the metropolitan area, which are in urban high status neighbourhoods in the city of Amsterdam, are self-employed (independent) and high-income lawyers, followed by high-income professionals in the arts and book publishing sectors, as well as highest income class fractions employed at the university. Self-employed architects are most overrepresented in suburban high status neighbourhoods in Amsterdam.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.128 | 0.069 |
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".