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Record W7020364816

Leading with Landscape: Enhancing the Process for Cultural Landscape Adaptive Reuse in Ontario

2022· dissertation· en· W7020364816 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptive reuseProcess (computing)ReuseLegislatureCultural landscapeAdaptation (eye)Landscape assessment
DOInot available

Abstract

fetched live from OpenAlex

Ontario’s cultural landscapes are evolving places facing challenges of growth and conservation. While other jurisdictions have moved toward more integrated approaches that center cultural landscape conservation within the broader spatial planning process, Ontario’s legislative framework and guidance can result in a siloed approach. The goal of this thesis is to critique the current process and suggest next steps for a holistic, integrated, and future-oriented process for the adaptive reuse of post-institutional cultural landscapes in Ontario. This will draw upon other Canadian and international landscape approaches that consider ecological, social, cultural and economic factors. This research uses mixed-methods including a literature scan, process mapping, an Ontario cultural landscape practitioner focus group, analysis, synthesis, and reflection. This research puts forward recommendations that build on current cultural landscape practice, which are intended to serve as a reference for practitioners in developing their own approaches to adaptive reuse projects that lead with landscape.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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