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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<br/>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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0100.001
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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 teacher head, not a consensus.

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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