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Record W2014122367 · doi:10.6000/1929-4409.2014.03.33

Violence and Healing: Exploring the Power of Collective Occupations

2014· article· en· W2014122367 on OpenAlexvenueno aff
Mapheyeledi Motimele, Elelwani Ramugondo

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingNarrativeOccupational therapyFocus groupPhotovoiceConstruct (python library)PsychologyRefugeeCollective efficacySociologyStructural violenceSocial psychologyOccupational scienceCriminologyPolitical sciencePoliticsPsychiatry

Abstract

fetched live from OpenAlex

The effects of violence on the person are extensive, more so, for persons disabled through violence. Research in occupational therapy has shown the effectiveness of occupations in helping displaced refugees to construct new identities and navigate new ways of belonging within asylum countries. No research thus far has focussed on the role of occupation for healing in contexts of sustained violence. Aim: To explore the role of occupations in the healing journeys of people physically impaired by violence. Study Design: Qualitative; Narrative Inquiry. Methodology: Photovoice and Narrative Interviews. Data Analysis: Narrative-analytic methods were used to produce explanatory stories. Findings: These are presented with a specific focus on the impact/influence of violence on personal and societal occupational engagement and the restorative role of collective occupations within the participants’ healing journeys. The findings suggest a need to reframe violence as a collective occupation that dehumanizes, and healing as a collective process that (re)humanizes within a broader framework of Ubuntu as an interactive ethic. These findings call for a shift in focus for rehabilitation practices involving individuals disabled through violence, in contexts of sustained direct and structural violence such as South Africa.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.268
GPT teacher head0.502
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations14
Published2014
Admission routes1
Has abstractyes

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