Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,006 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,128 | 0,069 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».