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Record W4391544674 · doi:10.46743/2160-3715/2024.6791

A Culturally Grounded Approach to Nepalese Grandmothers’ Caring Work: Developing Dohori as a Narrative Methodology

2024· article· en· W4391544674 on OpenAlexaff
Kusum Bhatta

Bibliographic record

VenueThe Qualitative Report · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGrounded theoryNarrativeNarrative inquiryPsychologyQualitative researchSociologyWork (physics)PedagogyAnthropologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The social sciences have a growing seismic shift from prioritising positivist, objective, and generalizable knowledge to accepting subjective qualitative knowledge. This has given rise to various art-based methods that leverage multi-sensory storytelling/narrative. To further advance innovation in qualitative narrative methods, I will present the Dohori narrative, an indigenous Nepali poetic storytelling method for narrative research with older grandmothers doing care work. I start by presenting a discourse on Dohori to understand better the history and traditional and cultural underpinning of the method. Provided a brief background to Nepali grandmother immigrants and then discussed the promise of Dohori as a form of culturally relevant narrative interviewing with this population. To demonstrate this, I provide an examplar case study adopting a conventional narrative interview and then Dohori to show the differences. The study showed that Dohori has the potential to elicit stories, emotions, and tacit knowledge and access other areas of consciousness that traditional narrative interviews are not privy to. I conclude by arguing for the adoption of Dohori and similar dialogical poetic methods for research with indigenous populations, especially minority groups with limited voice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.366
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.219
GPT teacher head0.505
Teacher spread0.286 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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