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Record W4251567366 · doi:10.32920/ryerson.14644119.v1

Strategies to animate Toronto's transitional post-industrial waterfront

2021· preprint· en· W4251567366 on OpenAlexaffabout
Erin Tito

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicLandscape and Cultural Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnimationRedevelopmentSpace (punctuation)Ephemeral keyPort (circuit theory)Public spaceArchitectural engineeringCivil engineeringComputer scienceGeographyEngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Post-industrial waterfronts are spaces in transition. Waterfront land will be redeveloped eventually, and until that time, planners must tum to new approaches for these transitional spaces, with a goal to activate and animate them. Animation strategies can be used in any post-industrial or transitional space, but in waterfronts, they are essential. This paper discusses two case studies. Gas Works Park and Landscape Park Duisburg-Nord are public space projects in which animation techniques have fostered transformation and engagement of the public. Several typologies of post-industrial space illustrate the animation techniques described within the case studies. The paper evaluates these techniques or strategies and applies them to a post-industrial area slated for redevelopment, Toronto's Port Lands. Key Words: post-industrial space, waterfront, animation, loose space, ephemeral landscapes.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.918
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.056
GPT teacher head0.242
Teacher spread0.186 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes2
Has abstractyes

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