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Record W2128544564 · doi:10.33182/ml.v6i2.72

Power and politics in migration narrative methodology: Research with young Congolese migrants in Uganda

2009· article· en· W2128544564 on OpenAlexaff
Christina Clark‐Kazak

Bibliographic record

VenueMIGRATION LETTERS · 2009
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsYork University
Fundersnot available
KeywordsNarrativePower (physics)ConfidentialityPoliticsSociologyAnonymityQualitative researchGender studiesInvestment (military)Narrative inquiryPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

This paper explores the power dynamics inherent in qualitative research involving migration narratives. Drawing on the author’s experiences collecting life histories and constructing narratives of Congolese young people in Uganda, this article addresses the ethical and methodological issues of representivity, ownership, anonymity and confidentiality. It also explores the importance of investment in relationships in migration narrative research, but also the difficulties that arise when professional and personal boundaries become blurred.

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)0.020
metaresearch head score (Gemma)0.039
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: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0170.015
Scholarly communication0.0110.009
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.090
GPT teacher head0.415
Teacher spread0.325 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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