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Record W7135417854 · doi:10.26412/psr217.04

Which Came First, Neighbourhood or Community? —Community Construction in a Self-Built Neighbourhood

2022· article· en· W7135417854 on OpenAlexaff
Taru Silvonen

Bibliographic record

VenueBristol Research (University of Bristol) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsNeighbourhood (mathematics)UrbanizationEthnographyUrban planningSocial relationSocial changeSettlement (finance)Interpersonal tiesParticipant observation

Abstract

fetched live from OpenAlex

The interest in changing social ties in urban neighbourhoods has generated sociological debate for decades. This paper contributes to this debate by focusing on the relationship between community and neighbourhood formation in the development of an informal settlement. While informal urbanisation is widely researched, the attention is usually placed on urban planning and development rather than a socio-spatial aspect. Drawing on an ethnographic case study, this paper analyses the transformation of agricultural land to urban settlement following residents’ self-organisation in Mexico City. The case study shows how social ties developed alongside collaboration between residents, highlighting a relationship between the social and spatial processes. Collaborative processes from small neighbour groups to broader neighbourhood-wide projects that contributed to the delivery of basic services and urban infrastructure also enabled the formation of community support networks. The findings highlight the intertwined nature of community and neighbourhood formation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.017
Scholarly communication0.0070.006
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.114
GPT teacher head0.333
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueBristol Research (University of Bristol)Same topicUrban and Rural Development ChallengesFrench-language works237,207