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Record W7028964458

Greening the Inner city: Eco-Friendly Community Development

2011· article· en· W7028964458 on OpenAlexaboutno aff

Bibliographic record

VenueWinnSpace (University of Winnipeg) · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Mediterranean Archaeology and History
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionArticular cartilage damageTSG101HyporeflexiaGestational periodDiafiltration
DOInot available

Abstract

fetched live from OpenAlex

The second WIRA Summer Institute - Greening the Inner City: Eco-friendly Community Development – was held from June 2nd to 7th 2003. 
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\nThe WIRA Summer Institute is designed with a number of objectives in mind. The intention of each Summer Institute is to create a unique, shared learning experience for university students and community practitioners through a series of workshop-style sessions addressing a range of key issues related to community development in Winnipeg’s inner city. The Summer Institute aims to take a “hands-on” approach to learning, to combine classroom learning with “in the field” experience of Winnipeg’s inner-city communities, and to provide instruction that combines theory with practice.
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\nThe 2003 Summer Institute "Greening the Inner city: Eco-Friendly Community Developments" course is intended for community workers, residents and university students, and will explore issues of environmental sustainability in the inner city. By drawing extensively on case studies and field project work, this course will examine challenges and successes of environmentally-sensitive community development. Topics will include: strategies for ‘greening’ the neighbourhood, energy-efficient housing development, and the role of transportation. A focus throughout will be the potential to create jobs and build skills for community residents using environmentally-friendly community development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.189
Teacher spread0.121 · 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 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
Published2011
Admission routes1
Has abstractyes

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