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Record W4410028001 · doi:10.22329/csw.v25i1.8094

Beyond Disposable Social Work and Towards Ecosocial Work

2024· article· en· W4410028001 on OpenAlexvenueno aff
Joonmo Kang, Carolyn Lesorogol, Vanessa Fabbre

Bibliographic record

VenueCritical Social Work · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Social workSociologyEngineering ethicsPolitical scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This study is an inquiry into the role of social work in responding to the vulnerability of poor older adults living in jjokbang-chon, a neighborhood with a high concentration of informal housing in Seoul, South Korea, to extreme weather disasters. The findings presented in this research are derived from a year-long ethnographic study. The fieldwork consisted of living in the community, carrying out participant observations through working with social work-related agencies, and conducting interviews with community members. The findings showed that the government-funded social work agency is mainly rooted in charitable material aid, or what we call disposable social work, and is not only wasteful in terms of the environment but also disposable in the sense that rather than addressing the fundamental problems, it is a short-term reactionary response whose effects quickly fade away. We highlight the work by the local grassroots group as an example of ecosocial work practice, which is more inclusive of the community members by providing opportunities for political participation and decision-making, The findings illuminate the role of ecosocial work in addressing climate change-related disasters in marginalized communities that empower marginalized individuals and communities.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0100.002
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.038
GPT teacher head0.391
Teacher spread0.353 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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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