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Record W2024395619 · doi:10.7245/0026-2005-41.3.310

Public Perceptions of the Value of Urban Trees in the River Rouge Watershed

2013· article· en· W2024395619 on OpenAlexaff
Andrea Olive, Lara Rusch, ANGELA AYERS

Bibliographic record

VenueMichigan Academician · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOutreachTree plantingWatershedUrban forestryGovernment (linguistics)Urban forestValue (mathematics)GeographyPublic opinionPerceptionUrban green spaceEnvironmental planningEnvironmental resource managementPolitical sciencePoliticsBusinessForestryEconomic growthPsychologyMarketingEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

ABSTRACT This study examines attitudes toward urban green space among homeowners in the River Rouge Watershed of southeast Michigan. Trees play a significant role in urban areas by providing numerous environmental, economic and social benefits to community residents. But public attitudes toward trees are not well understood. Through survey research we found that residents are most likely to value trees for personal benefit (like shade) but that social reasons are also valuable (improving the neighborhood). We also illustrate that cost is not an impeding factor in willingness to plant trees. Finally, our study reveals that homeowners felt tree planting is both an individual responsibility as well as a government responsibility. On the basis of these findings, we recommend that urban forestry policy should focus on education and outreach in an attempt to marry tree-planting initiatives to the individual benefits of trees as well as the community benefits of urban green space.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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 designObservational
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

Citations5
Published2013
Admission routes1
Has abstractyes

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