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Record W2551410727 · doi:10.1177/1473325016678310

Social workers’ peculiar contribution to ethnographic research

2016· article· en· W2551410727 on OpenAlexaff
Gerald A. J. de Montigny

Bibliographic record

VenueQualitative Social Work · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsSociologySocial workEthnographyFace (sociological concept)Agency (philosophy)Grounded theoryEpistemologyCoherence (philosophical gambling strategy)MandateSocial psychologyQualitative researchPublic relationsSocial sciencePsychologyLawPolitical science

Abstract

fetched live from OpenAlex

Over generations, social workers have borrowed theories from sociology. However, sociologists have generally avoided borrowing theory from social work. By beginning with social work practice wisdom, we can unfold the complex elements organizing social work practice and by extension ethnographic research. Complexity and resulting uncertainty are antidotes for theoretical purity. Practice as grounded in life, that of client’s and social workers is inherently “dirty”, i.e., messy, disorganized, confusing, unfolding, and uncertain. Understandings and practices are accomplished in a connection of self to a profession, agency/organization, mandate and purpose, and ethical orientation, in interaction with colleagues and clients. Social workers take sides as they are grounded in an ethic of care. The challenge of developing an ethical practice in the face of difference, disagreement, disjunction, and conflict lead social workers to bracket, and hence reflect on the putative coherence of a “life world.” Face-to-face work with individuals rather than being a liability provides a source of knowledge and wisdom to inform social science generally.

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)0.082
metaresearch head score (Gemma)0.165
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.165
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0130.008
Science and technology studies0.0110.028
Scholarly communication0.0090.010
Open science0.0030.013
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.277
GPT teacher head0.579
Teacher spread0.302 · 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.

Study designQualitative
DomainMethods
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

Citations9
Published2016
Admission routes1
Has abstractyes

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