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Social Geographies of Education: Looking Within, and Beyond, School Boundaries

2008· article· en· W2030951078 on OpenAlexafffund
Damian Collins, Tara Coleman

Bibliographic record

VenueGeography Compass · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Alberta
FundersSimon Fraser University
KeywordsSpace (punctuation)PoliticsSociologyPower (physics)Gender studiesReproductionSocial sciencePolitical scienceEcologyLaw

Abstract

fetched live from OpenAlex

Abstract Schools have received less attention from geographers than institutions such as the clinic and the hospital – despite the fact that, for most people, encounters with sites of medicine are rarer than encounters with sites of education. Indeed, schools are central to the geographies of children and young people, and to the organization of much family life. Moreover, they play a central role in shaping social identities. In this article, we provide an introduction to, and review of, the literature that takes seriously the sociospatial dimensions of schooling. Our discussion is organized around two central themes: first, the organization of school space, and the ways in which it is implicated in issues of power, and the reproduction of preferred identities; second, the linkages between schools and broader communities, and what these tell us about the values and aspirations attached to schooling. In both respects, we suggest, schools are places of considerable social and political significance.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0040.027
Scholarly communication0.0100.008
Open science0.0010.005
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 head0.019
GPT teacher head0.292
Teacher spread0.272 · 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

Citations193
Published2008
Admission routes2
Has abstractyes

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