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Record W2360430871 · doi:10.2304/pfie.2014.12.7.933

The Role of Education for Democracy in Linking Social Justice to the ‘Built’ Environment: The Case of Post-Earthquake Haiti

2014· article· en· W2360430871 on OpenAlexaffabout
Paul R. Carr, Gary Pluim, Gina Thésée

Bibliographic record

VenuePolicy Futures in Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversité du Québec à MontréalLakehead UniversityUniversité du Québec en Outaouais
Fundersnot available
KeywordsDemocracyContext (archaeology)SociologyEconomic JusticeEnvironmental justiceSocial environmentSocial justiceEnvironmental ethicsSocial sciencePublic administrationPolitical sciencePoliticsLawGeography

Abstract

fetched live from OpenAlex

The manner in which the built environment is constructed has a tremendous effect on the degree to which health, wealth and social outcomes are distributed within a society. This is particularly evident when a crisis of the natural environment affects the built environment, as was the case after the Haitian earthquake of 2010. Understanding the consequences of the earthquake as socially precipitated rather than a natural occurrence requires a paradigm shift, a project for educational policy, pedagogy and epistemology. In particular, education for democracy in its broadest sense can serve to re-align thinking towards understanding the connection between the built environment and social justice. In this article the authors present their research with teacher-education candidates and the candidates' perspectives and experiences of education for democracy at a Canadian university. In relating these perspectives to the possibilities for contextualizing the aftermath of the earthquake in Haiti, the authors propose educational policy solutions that highlight a thick democracy, social justice, the role of context and history, and a more concrete connection with public health.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.454
Teacher spread0.401 · 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 designTheoretical or conceptual
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

Citations3
Published2014
Admission routes2
Has abstractyes

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