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Record W2135943738 · doi:10.1093/cdj/bst001

When words arrive: a qualitative study of poetry as a community development tool

2013· article· en· W2135943738 on OpenAlexafffundabout
Sandra Sjollema, Jill Hanley

Bibliographic record

VenueCommunity Development Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity and Sustainable Development
Canadian institutionsMcGill UniversityCommunity Based Research Centre
FundersMcGill University
KeywordsPoetryThe artsCommunity developmentContext (archaeology)SociologyCreative writingQualitative researchAction (physics)CreativityVisual artsPsychologySocial scienceSocial psychologyArtLiteraturePolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

Poetry, among the arts, remains understudied as a means for community development. To address this scarcity, this paper considers the use of poetry as a community development tool and discusses its uniqueness in this role. It offers a description and analysis of an exploratory, qualitative research study carried out with twelve respondents in Montreal, Canada, who participated in community-based creative writing groups. Evaluation suggested that, overall, the poetry groups made a positive contribution to community building and development. This paper locates the study in the context of community development and the arts and includes references to poetry therapy and social action-based creative writing. It also raises questions as to why poetry has not found its place on the agenda of arts-based community development.

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)0.021
metaresearch head score (Gemma)0.040
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 score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0150.016
Scholarly communication0.0070.005
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.070
GPT teacher head0.375
Teacher spread0.305 · 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

Citations40
Published2013
Admission routes3
Has abstractyes

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