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Record W7097234485

TEACHING STUDENT ARCHITECTS ABOUT RECONSTRUCTION – A SYSTEMS APPROACH

2015· article· en· W7097234485 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)EnthusiasmProcess (computing)Domain (mathematical analysis)Work (physics)Focus (optics)Natural (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Reconstruction after natural disasters requires a broad view of the issues and the possibilities; it cannot be reduced to the single levels of techniques or of social issues, taken in isolation, demanding instead the mobilization of efforts of analysis and synthesis, coupled to organizational and physical design. Architect students potentially possess the ability to take a broad-scope view of an environmental design problem, but habitually focus primarily on technical and esthetic design issues rather than broadly including organizational and process design as the systems approach suggests. At the School of Architecture, University of Montreal, we offer the opportunity for students to broaden their view of their future domain of professional responsibility, by harnessing their skill and enthusiasm to the humanitarian problem of post-natural-disaster reconstruction in developing countries. The scenario within which they work comprises two phases: (i) developing the conditions of a competition (in the form of a performance specification) and (ii) developing a technical and logistical response accompanied by an organizational design. Through the students ’ work, several principles underlying post-disaster reconstruction have emerged, such as its open-ended time-frame, the need to broaden what “housing ” includes, the importance of organizational design and the scope for a systemic view of local involvement.

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)0.000
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 score0.582
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.364
Teacher spread0.305 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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