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Record W6973308766 · doi:10.5751/es-14579-280429

Greening practitioners worry about green gentrification but many don’t address it in their work

2023· article· en· W6973308766 on OpenAlexaffabout

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsGentrificationGreen infrastructureGreeningNeighbourhood (mathematics)Work (physics)WorryDisinvestment

Abstract

fetched live from OpenAlex

As cities attempt to ameliorate urban green inequities, a potential challenge has emerged in the form of green gentrification. Although practitioners are central to urban greening and associated gentrification, there has yet to be an exploration of practitioner perspectives on the phenomenon. We fill this gap with an online survey of 51 urban greening practitioners in Metro Vancouver and the Greater Toronto Area. Most respondents defined green gentrification as the displacement of vulnerable residents due to the installation or improvement of green space that attracts wealthy in-movers and increases property values. They were most likely to identify greening as driving green gentrification, with a minority identifying other systemic drivers with greening in a secondary role. Although 39 of 51 participants had some familiarity with green gentrification, most reported low confidence in their understanding of the concept, little evidence of using the concept in their work, and moderate concern that their work is implicated in green gentrification. The gentrification issues most encountered by practitioners were changes to neighbourhood character and uneven investment in public infrastructure, and those working in domains linked to planning, equity, and engagement were most likely to encounter gentrification issues. Practitioners experienced multiple barriers to addressing green gentrification, including limited institutional capacity, limited access to data and relevant information, policy/mandate restriction, and lack of engagement tools. Results indicate that practitioners have a moderate understanding of green gentrification but do not often use the concept in their work, despite their potential to contribute to or exacerbate it. This suggests some resistance to critiques of urban greening practice, a failure of scholarly critiques of urban greening to influence policy change, and the need for stronger research theory and research co-creation involving practitioners and academia.

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)0.010
metaresearch head score (Gemma)0.031
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 score0.023
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.031
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.008
Scholarly communication0.0060.008
Open science0.0020.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.003

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 head0.241
GPT teacher head0.498
Teacher spread0.257 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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