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Record W4285009463 · doi:10.22215/etd/2022-15067

Beyond the Pristine: Reframing the Notion of Nature Conservation through the Agency of Plant 'Vagabonds' in Toronto's Rouge National Urban Park

2022· dissertation· en· W4285009463 on OpenAlexaboutno aff
Saman Soltani

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingAgency (philosophy)Context (archaeology)GeographyPolitical ecologyLandscape architectureNational parkPoliticsEnvironmental ethicsSociologyEcologyPolitical scienceSocial scienceArchaeology

Abstract

fetched live from OpenAlex

Protecting nature in its pristine state and within designated geographic boundaries is embedded within historical framings of conservation. Yet, in the context of today's rapid anthropogenic change, this concept is increasingly flawed and irrelevant. This thesis draws from interdisciplinary literature in political ecology, geography, and landscape architecture to explore Rouge National Urban Park, Toronto's newest category of urban nature preserve. It foregrounds Botanist Gilles Clément's research on "vagabonds" as valuable ruderal species with design agency. Using fieldwork, mapping, document analysis, and model making as catalysts for design intervention, the work proposes the park's transformation into a vagabond living lab, employing a network of experimental design instruments across the landscape. Each design frames the site as a testing ground for new understandings of nature conservation within urban contexts—done by exploring the role of vagabonds. Ultimately, this thesis speculates what future urban landscapes can be like in conditions of environmental flux.

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)0.003
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 score0.731
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.029
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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
Published2022
Admission routes1
Has abstractyes

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