MétaCan
Menu
Back to cohort
Record W2947676566 · doi:10.1177/1468794119851330

Discovering dimensions of research ethics in doing oral history: going public in the case of the Ghent orphanages

2019· article· en· W2947676566 on OpenAlexfundno aff
Lieselot De Wilde, Griet Roets, Bruno Vanobbergen

Bibliographic record

VenueQualitative Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaAlberta Centre for Child, Family and Community Research
KeywordsOral historySituational ethicsInterpretation (philosophy)Public historySociologyPoliticsResearch ethicsComparative historical researchSocial scienceEnvironmental ethicsEngineering ethicsLawPolitical scienceMedia studiesAnthropology

Abstract

fetched live from OpenAlex

In this article, we argue that research ethics in the doing of oral history research are inadequately addressed in the existing body of research. Although oral history researchers have paid considerable attention to procedural ethical issues, there is currently a lack of attention on situational research ethics in the doing of oral history. We address particular ethical challenges that we experienced while reconstructing the history of three remaining orphanages after the Second World War in the city of Ghent (a city in Flanders, the Dutch speaking part of Belgium) by drawing on oral history research from former orphans and ex-staff members. Their rather surprising, yet pertinent, questions enabled us to discover the political nature of research ethics, and prompted us to engage in ‘going public’. We discuss the complexities of our attempt to provide a ‘questionable’ historical interpretation for the ambiguous history of these childhood institutions in the recent past.

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)0.039
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.042
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0300.094
Scholarly communication0.0150.015
Open science0.0030.014
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.525
GPT teacher head0.614
Teacher spread0.089 · 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
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

Explore more

Same venueQualitative ResearchSame topicData Analysis and ArchivingFrench-language works237,207