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Record W2946816225 · doi:10.4103/intv.intv_17_18

Witnessing the vulnerabilities and capabilities of one Afghan woman: Cultural values as a source of resilience in daily life

2018· article· en· W2946816225 on OpenAlexaff
Sakiko Yamaguchi

Bibliographic record

VenueIntervention · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsAfghanFriendshipDistressLegal guardianContext (archaeology)Psychological resiliencePsychologyPoliticsThrivingSocial psychologySociologyPolitical scienceLawHistorySocial science

Abstract

fetched live from OpenAlex

This personal reflection on my daily interactions with an Afghan woman, Bibi Hawa, aims to describe how I witnessed her psychological distress, partly manifested as chest pain, and her resilience to this distress in a particular Afghan socio-cultural and political context. My reflections shed light on the importance of finding a space in which resilience can be built. As mutual trust, friendship and a reciprocal guardianship developed with Bibi Hawa, I came to recognise her capabilities as a woman, mother, friend, housekeeper, breadwinner and co-worker as well as the way in which she was able to move forward by fostering resilience through building upon her own abilities and the Afghan cultural values of family unity, perseverance in overcoming challenges and dedication in fulfilling responsibilities. This reflection reiterates the importance of rethinking the ways in which cultural values can enhance resilience and the need to find the means and space to cultivate these cultural values as a source of resilience in daily life.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.014
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0140.019
Scholarly communication0.0050.004
Open science0.0010.008
Research integrity0.0020.007
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 head0.034
GPT teacher head0.390
Teacher spread0.356 · 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
Published2018
Admission routes1
Has abstractyes

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