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Record W3080980893 · doi:10.1177/1049732320949167

Unsettling Knowledge Synthesis Methods Using Institutional Ethnography: Reflections on the Scoping Review as a Critical Knowledge Synthesis Tool

2020· review· en· W3080980893 on OpenAlexafffund
Nicole Dalmer

Bibliographic record

VenueQualitative Health Research · 2020
Typereview
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaMedical Library Association
KeywordsReflexivityEthnographySociologyPositivismKnowledge managementProcess (computing)EpistemologyEngineering ethicsComputer scienceSocial scienceEngineering

Abstract

fetched live from OpenAlex

Scoping reviews are an increasingly popular knowledge synthesis method. While knowledge synthesis methods abound in evidence-based practices, these methods are critiqued for their reliance on positivism. Drawing on a scoping review that mapped scholarly conceptualizations of family caregivers' information-related dementia care work, in this article, I reconcile institutional ethnography's epistemological and ontological assumptions with the prescribed scoping review framework. I first explore the textual organization of scoping reviews. I then unpack the process of modifying three scoping review stages in keeping with an institutional ethnography method of inquiry, and in doing so, transform the scoping review into a critical knowledge synthesis tool. Through a reflexive process, I deconstruct scoping review's textual authority and uncover that scoping reviews bring about a double decontextualization of family caregivers' information work, removing family caregivers from their experiences of their information-related care work while simultaneously reducing them to objects of techno-scientific interventions.

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)0.320
metaresearch head score (Gemma)0.534
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3200.534
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.011
Science and technology studies0.0130.011
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0010.001

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 head0.954
GPT teacher head0.834
Teacher spread0.120 · 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
GenreReview

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

Citations22
Published2020
Admission routes2
Has abstractyes

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