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Record W2074417302 · doi:10.1386/adch.7.2.91_7

Social justice agency in the landscape architecture studio: an action research approach

2009· article· en· W2074417302 on OpenAlexaff
Karen Wilson Baptist, Hala Nassar

Bibliographic record

VenueArt Design & Communication in Higher Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSociologyAgency (philosophy)Transformative learningPoliticsArchitectureRigourAction (physics)InjusticeValue (mathematics)Environmental ethicsEconomic JusticeSocial sciencePedagogyEpistemologyPolitical scienceLawVisual arts

Abstract

fetched live from OpenAlex

In landscape architecture education we often make reference to passion, to imagination and to rigour as desirable attributes in our students. But what of cultivating a capacity to care? Lynn Davies, in Building a Civic Culture Post-Conflict, urges educators to initiate a new normality of cooperation (Davies 2004, p. 243), where students are challenged to take on roles as active citizens who will challenge social injustice (Davies 2004, p. 229). In our discussions regarding the application of Davies' work to landscape architecture, we anticipated that memorial sites, because of their strong social, political, symbolic and emotional associations, would provide a transformative means of introducing students to critical issues that affect the human experience of public space. Moreover, we anticipated that the educational value of these sites could be intensified through the application of peace pedagogy. Employing a practical action research methodology we critically reflect upon the impact of the successes and failures of a landscape architecture design studio conceived in the spirit of a social action agenda of peace and commemoration.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.788
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.524
GPT teacher head0.528
Teacher spread0.003 · 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.

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

Citations7
Published2009
Admission routes1
Has abstractyes

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