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Record W2114139268 · doi:10.1177/0013916514546744

Smokestacks, Parkland, and Community Composition

2014· article· en· W2114139268 on OpenAlexaboutno aff
Cassandra Johnson Gaither

Bibliographic record

VenueEnvironment and Behavior · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental justiceCensusQuarter (Canadian coin)GeographyEquity (law)PovertyAmerican Community SurveyEconomic growthSocioeconomicsPolitical scienceSociologyDemographyPopulationArchaeologyEconomics

Abstract

fetched live from OpenAlex

This case study addresses environmental equity, in terms of African American, Latino, White, and poor communities’ proximity to both industrial facilities and parkland in Hall County, Georgia, USA. The project’s two primary goals are to (a) expand environmental justice analyses to account for both environmental burdens (industrial sites) and benefits (parkland acreage), and (b) extend this broader investigation to the county’s emergent Latino populations. Results show that both Blacks and Latinos are overrepresented in census block groups (CBGs) within 1 mile of industrial facilities, while Whites are underrepresented. Conversely, Latinos and those near or below poverty are, on average, underrepresented in communities within one-quarter mile of parkland, but Whites are overrepresented. This article discusses the environmental justice and planning implications of these findings in terms of converting existing land uses to urban green space and fuller participation of minorities in such decision making.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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