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Record W4255159752 · doi:10.32920/ryerson.14657382.v1

In my back yard

2021· preprint· en· W4255159752 on OpenAlexaffabout
John Verhaeven

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsToronto Metropolitan UniversityUniversity of Victoria
Fundersnot available
KeywordsYardMountNeighbourhood (mathematics)Context (archaeology)AcreGeographyWildlifeVisual artsWork (physics)SociologyArchaeologyCartographyHistoryEngineeringArt

Abstract

fetched live from OpenAlex

In My Back Yard is a documentary film that explores the changing landscape of the Mount Dennis neighbourhood in Toronto. This change is represented by the 54-acre Kodak site that is being transformed into the second largest transportation hub in the Greater Toronto Area. The film employs a series of visual strategies and retells recent observations related to the impact of this massive infrastructure project on the people, the land and the urban wildlife. Local residents, politicians and community leader were consulted. Their interviews are combined with dioramas, archival photographs and time-lapse photography to express the multi-facetted list of community concerns. This support paper attempts to define and analyse these struggles within the context of Leo Marx’s 1964 work The Machine in the Garden: Technology and the Pastoral Ideal in America. This paper proposes that Marx’s concept of the “middle landscape” helps to define the current struggle in Mount Dennis.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.212
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2120.040

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 head0.048
GPT teacher head0.237
Teacher spread0.189 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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