MétaCan
Menu
Back to cohort
Record W4232427587 · doi:10.32920/ryerson.14648361

The role of transitional uses, temporary interventions & "ephemerality" in post-industrial waterfront spaces: lessons for Toronto's port lands.

2021· preprint· en· W4232427587 on OpenAlexaffabout
Kathryn J. Hickey

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsRedevelopmentEphemeral keyPort (circuit theory)Futures contractPsychological interventionEnvironmental planningBusinessUrban regenerationEngineeringCivil engineeringGeographyComputer scienceFinanceComputer security

Abstract

fetched live from OpenAlex

Post-industrial waterfronts are often characterized by a time-gap or a moment of standstill between the collapse of a previous use and the transition to a new and future use. However, conventional planning processes often leave these areas in a curious limbo while they are being prepared or while their futures are being determined. Changing contemporary conditions demand that planners re-evaluate urban planning and development approaches. Transitional uses and temporary interventions must be recognized as legitimate and important aspects of the planning process especially in these ephemeral landscapes as they provide an outlet for innovative and adaptive practices. This paper discusses three case studies. The cities of Melbourne, Amsterdam and Hamburg implemented unique and adaptive projects along their waterfronts as mechanisms to catalyze redevelopment and foster social engagement during indeterminate times. This paper explores these projects and applies the strategies used in each to Toronto’s vacant and extensively underutilized Port Lands.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.827

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 studies0.0120.020
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.291
Teacher spread0.227 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicMaritime Ports and LogisticsFrench-language works237,207