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Record W2947149000 · doi:10.15402/esj.v5i2.68348

Woolly Stories: An Art-Based Narrative Approach to Place Attachment

2019· article· en· W2947149000 on OpenAlexvenueno aff
Kendra D. Stiwich, J. McCunn Lindsay, Chantey Dayal

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePlace attachmentTheme (computing)InstitutionPsychologySocial psychologyConnection (principal bundle)Sense of placeSociologyAestheticsArtLiteratureSocial scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

When people join an institution, no assurance of positive social connection exists. The mechanisms of psychological attachment to institutions are not well understood. However, place attachment is a predictor of individual well-being and, when correlated with life satisfaction and neighborhood ties, can enhance civic engagement and social trust. Research suggests that narratives can be a symbolic mechanism of place attachment. Thus, to increase place attachment in the parent population at a small elementary school, various art-based narrative activities were carried out as part of the OurSchoolOurStories project. Creating a storied blanket was one activity. Seven women needle-felted nine squares with the theme of representing some aspect of what the school meant to them. In a circle, they shared many stories including where they came from, how they came to be at the school, and their experiences at the school. Through these artistic narratives, participants were able to share much about their place identities, which allowed for social connection, and a sense of integration within the group.

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)0.007
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0080.014
Scholarly communication0.0120.009
Open science0.0030.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.145
GPT teacher head0.447
Teacher spread0.302 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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