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Record W2001978108 · doi:10.3138/chr.92.4.607

An Intimate Understanding of Place: Charles Sauriol and Toronto's Don River Valley, 1927–1989

2011· article· en· W2001978108 on OpenAlexvenueaboutno aff
Jennifer Bonnell

Bibliographic record

VenueCanadian Historical Review · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsWildernessMemoirWoodlandRiver valleySymbol (formal)GeographyNatural (archaeology)BiographyHistorySociologyArchaeologyEcology

Abstract

fetched live from OpenAlex

Every summer from 1927 to 1968, Toronto conservationist Charles Sauriol and his family moved from their city home to a rustic cottage just a few kilometres away, within the urban wilderness of Toronto’s Don River Valley. In his years as a cottager, Sauriol saw the valley change from a picturesque setting of rural farms and woodlands to an increasingly threatened corridor of urban green space. His intimate familiarity with the valley led to a lifelong quest to protect it. This paper explores the history of conservation in the Don River Valley through Sauriol’s experiences. Changes in the approaches to protecting urban nature, I argue, are reflected in Sauriol’s personal experience – the strategies he employed, the language he used, and the losses he suffered as a result of urban planning policies. Over the course of Sauriol’s career as a conservationist, from the 1940s to the 1990s, the river increasingly became a symbol of urban health – specifically, the health of the relationship between urban residents and the natural environment upon which they depend. Drawing from a rich range of sources, including diary entries, published memoirs, and unpublished manuscripts and correspondence, this paper reflects upon the ways that biography can inform histories of place and better our understanding of individual responses to changing 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.995

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.216
Teacher spread0.183 · 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
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

Citations3
Published2011
Admission routes2
Has abstractyes

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