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Record W2759084135 · doi:10.1080/02697459.2017.1378975

Embedding Artists within Planning: Calgary’s Watershed+ Initiative

2017· article· en· W2759084135 on OpenAlexaffabout
Jason F. Kovacs, Jeffrey Biggar

Bibliographic record

VenuePlanning Practice and Research · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Politics, and Modernism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWatershedTransferabilityPublic artPublic participationEmbeddingPublic relationsSociologyEnvironmental planningPolitical scienceVisual artsGeographyArtComputer science

Abstract

fetched live from OpenAlex

Since 2011, artists have been embedded within Calgary’s Utilities and Environmental Protection department as part of the public art program Watershed+. In particular, artists are given workspaces alongside municipal staff to encourage interaction and new ideas. Watershed+ has led to innovative examples of public art that are meant to provoke thought about the local environment. Through interviews with artists and municipal staff engaged in the project, this paper explores the specific avenues through which the initiative has embedded artists within municipal planning. The paper also considers the transferability of the Watershed+ model to other cities.

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)0.007
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0280.025
Scholarly communication0.0120.003
Open science0.0030.018
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0120.001

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 head0.353
GPT teacher head0.472
Teacher spread0.119 · 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 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

Citations10
Published2017
Admission routes2
Has abstractyes

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