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Record W2995695399 · doi:10.1215/15366936-7789739

Listening in Arabic

2019· article· en· W2995695399 on OpenAlexaboutno aff
Neda Maghbouleh, Laila Omar, Melissa A. Milkie, Ito Peng

Bibliographic record

VenueMeridians · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyActive listeningCitizenshipParticipatory action researchNarrativeAction researchArabicCitizen journalismPedagogyGender studiesLinguisticsPolitical scienceCommunication

Abstract

fetched live from OpenAlex

Abstract This article reflects upon three developments emergent from a feminist approach in research with Syrian newcomer mothers in Toronto, Canada. First, a feminist approach shapes how the authors build their research team and facilitate internal meetings as a diverse, multigenerational group open to learning from others. Second, a feminist approach requires that the authors center mothers’ words through the critical practice of ensuring shared Arabic language and local knowledge in the research process. The authors offer excerpts in Arabic and English from participants’ narratives to describe how giving nuance to multiple forms of expression is key to a feminist practice of translation. Third, the authors describe how this approach opens their project to involve a range of participatory-action activities driven by the voices and desires of participants. The authors end by summarizing their ethical and methodological practices in light of inequalities at the intersection of citizenship status, class, nation, race, and other categories of asymmetrical power. These inequalities shape the authors’ attempts to reorganize conventional participant-researcher and student-faculty dynamics in their work together.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0670.015

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 head0.093
GPT teacher head0.503
Teacher spread0.410 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2019
Admission routes1
Has abstractyes

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