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Record W2076932495 · doi:10.1080/00750770209555802

Identifying dimensions of urban social change in Dublin‐1986 to 1996

2002· article· en· W2076932495 on OpenAlexafffund
Peter Kitchen

Bibliographic record

VenueIrish Geography · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsCensusGeographySocial changeDemographic changeSocioeconomicsSpatial changeRegional sciencePopulationEconomic geographyDemographySociologyEconomic growthPhysical geographyEconomics

Abstract

fetched live from OpenAlex

Since the mid 1980s, Ireland has been subjected to significant social, economic and demographic change. The transformation was especially apparent in Dublin, the country's largest and most prominent urban centre. The paper employs small area statistics from the 1986 and 1996 censuses and adopts a factorial ecological approach to investigate the nature and geography of urban social change in the Dublin urban region. Four principal axes or dimensions of change were identi‐fied: ‘Family status’, ‘Socio‐economic status’, ‘Demographic change’, and ‘Seniors/Retirement’. While the study found that overall, the Dublin urban region was characterised by stability between 1986 and 1996, a number of significant spatial variations of change were evident in the four Local Authority Areas under study, particularly in Dun Laoghaire‐Rathdown and South Dublin. The paper also proposes several avenues for further research including an update of urban social change using data from the 2002 Census of Population when it becomes available.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.632

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.343
Teacher spread0.232 · 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 designObservational
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

Citations11
Published2002
Admission routes2
Has abstractyes

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