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Record W2512015281 · doi:10.1386/jaah.7.1.55_1

Re-stitching and strengthening community: Three global examples of how doll-making translates into well-being in Indigenous cultures

2016· article· en· W2512015281 on OpenAlexaffabout
Sujane Kandasamy, Sonia S. Anand, Gita Wahi, Kate Wells, Kirsty G. Pringle, Loretta Weatherall, Lyniece Keogh, Jessica H. Bailey, Kym Rae

Bibliographic record

VenueJournal of Applied Arts and Health · 2016
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIndigenousZuluSociologyIdentity (music)SilenceSpiritualityGender studiesAestheticsMedicineArt

Abstract

fetched live from OpenAlex

Abstract Traditional doll-making has important meanings that translate into personal and communal identity. As one of the earliest discovered play artefacts, dolls are deeply intertwined with symbolic meanings around spirituality, rituals, familial histories and communal traditions. These values are especially important in Indigenous groups where health and well-being pivots on the preservation of cultural heritage. This article develops the theory on the well-being functions of doll-making through the exploration of three different practices in Indigenous cultures across the globe. We explore the Gomeroi Yarning dolls (Australia), Six Nations Cornhusk dolls (Canada) and Siyazama Zulu dolls (South Africa) to show that, through building the expression of local community-level identity, these dolls support Indigenous world-views around well-being. Specifically, the Gomeroi Yarning dolls encourage the sharing of oral personal narratives, the Six Nations Cornhusk dolls promote the transmission of cultural teachings, and the Siyazama Zulu dolls create community support networks through locally relevant HIV/AIDS awareness. As a result, local Indigenous communities are strengthened through the space that is created for a healing process, capacity building for problem-solving, and the reclaiming of Indigenous identity. All of these factors are important steps for moving forward from the silence, dealing with trauma and difficult situations, and thus transforming pain and grief through cross-cultural communication.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.022
Scholarly communication0.0030.004
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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