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Institutional ethnography

2012· other· en· W4255432353 on OpenAlexaboutno aff
Peter R. Grahame, Kamini Maraj Grahame

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2012
Typeother
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographySociologyEthnomethodologySocial worldsSocial scienceGender studiesEpistemologyAnthropology

Abstract

fetched live from OpenAlex

The term “institutional ethnography” has been used since 1987 to refer to an approach to social inquiry pioneered by Canadian sociologist Dorothy E. Smith (Campbell & DeVault 2011; Smith 1987). By the 1980s, Smith had become known internationally for studies in the social organization of knowledge with roots in the women's movement, Marx, phenomenology, and ethnomethodology. The central concerns of this approach included beginning with the standpoint of women, treating social actors as active knowers of their worlds, and examining the social relations of knowledge – especially the gender‐based divide between everyday worlds of local experience and the ruling forms of institutional organization in capitalist societies. Institutional ethnography both continues this project and refocuses it, placing emphasis on (1) ethnography as an approach to studying social organization and (2) a conception of institutional processes in which text‐based forms of coordination play a key role. Since the 1980s, a significant body of work in institutional ethnography has been built up, numerous institutional ethnography‐based dissertations have been undertaken, several international conferences on institutional ethnography have been held, journal special issues on institutional ethnography have been published, and institutional ethnography sections of the Society for the Study of Social Problems and the International Sociology Association have been established.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.588
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.017
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.117
GPT teacher head0.461
Teacher spread0.343 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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