MétaCan
Menu
← Back to cohort
Record W2738696796

Arts-Based Gentrification in Hamilton, ON

2017· article· en· W2738696796 on OpenAlexaboutno aff
Jacob Richard Swinson Ryan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsGentrificationThe artsSociologyImmigrationMedia studiesSocial scienceGender studiesPolitical scienceEconomic geographyGeographyEconomic growthLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper offers an in-depth exploration of arts-based gentrification in Hamilton, Ontario, with particular attention to the city’s emerging James Street North Arts District. Although the city is following an economic trajectory similar to that of other post-industrial cities in Canada and the United States, it remains largely outside the scope of contemporary urban studies; a gap in research this paper aims to begin to bridge. Despite the dearth of academic work on gentrification in Hamilton, it is a topic widely acknowledged and debated in colloquial discourse within the city, including in local news media, activist publications, and hardcore punk music; all of which are examined and contextualized within both regional demographic studies and broader theories of post-industrial gentrification. The detriments of gentrification in Hamilton are grouped into three broad categories: economic displacement, cultural shifts, and erosions of civic rights and freedoms. From here, conclusions are drawn that gentrification has disproportionately negative effects on racialized populations, immigrants, and the urban poor, and that further studies, both qualitative and quantitative, should be undertaken with the ultimate goal of proactive policy change.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.158
GPT teacher head0.390
Teacher spread0.232 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicCultural Industries and Urban Development→French-language works237,207→