MétaCan
Menu
Back to cohort
Record W7024865324

Toronto’s Port Lands: Climate Change and a Transition in the Nature/Culture Relationship

2022· other· en· W7024865324 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typeother
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)Climate changeIdeologyFloodplainFlood mythLand use, land-use change and forestryLand use
DOInot available

Abstract

fetched live from OpenAlex

On Toronto’s east-central waterfront, the Port Lands project is transforming an area that used to be considered one of the most underused parts of the city. The project aims to protect the surrounding areas from the Don River floodplain by rerouting the river and renaturalizing the landscape that surrounds it. The Port Lands project is heavily shaped by the anticipated pressures that climate change will place on the cities’ infrastructure and ecological systems. Understanding the Port Lands within the framework of a historical trajectory of land use, from industry to vacancy, then vacancy to now a utopian vision of a ‘green’ future community illustrates how transformations in the ideologies of nature are being transcribed onto the cityscape. The main objective of this study is to investigate whether climate change is enacting transformations to the ways in which we see the nature/culture relationship. Through an analysis of the historical context, the policy contexts, and the larger dynamics at play in the Port Lands, this study shows how constructed ideas of nature and landscape are shifting to incorporate climate change strategies and accommodate urban natures. The Port Lands project, both as a physical change in the landscape as well as in its Planning Framework as a projection of an idealized future, exemplifies a transition in the ideology of nature which is being catalyzed by climate change.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.449
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.155
Teacher spread0.145 · 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 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueYork University Digital Library (York University)Same topicAdvanced Materials and MechanicsFrench-language works237,207