MétaCan
Menu
Back to cohort
Record W2164878690 · doi:10.1525/jer.2007.2.1.3

Sharing Data and Results in Ethnographic Research: Why This Should Not Be an Ethical Imperative

2007· article· en· W2164878690 on OpenAlexafffund
Matthew Cooper

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsMcMaster University
FundersMcMaster UniversityU.S. Department of Homeland Security
KeywordsEthnographyVariety (cybernetics)SociologyEngineering ethicsResearch ethicsRelation (database)PoliticsField (mathematics)EpistemologyPublic relationsPolitical scienceLawComputer scienceAnthropology

Abstract

fetched live from OpenAlex

RESEARCHERS RECENTLY HAVE argued that offering to share research results with study participants should be an "ethical imperative." This article considers that suggestion in light of the practice of ethnographic, particularly anthropological, research. Sharing results is discussed in relation to several issues, e.g., whether it occurs during or after completion of a project, whether the research is long-term, the complexities involved in depositing field materials in archives, the changing politics of ethnographic research, research not concerned with communities, situations in which participants and the anthropologist may be in danger, and changing styles of ethnographic research. I argue that, ideally, sharing should be a regular component of ethnographic research but should not be an ethical requirement. Given the complexity, variety and changing political contexts of ethnographic research, implementing such a requirement would often be practically impossible and sometimes would be inadvisable. I recommend instead that research ethics boards educate themselves about the nature of ethnographic research. Further, they should approach decision making on the issue of data or results sharing on a case-by-case basis. For researchers, I recommend that discussion of data and result sharing should become part of the education of all ethnographers and that discussion of the issue should be fostered.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchOpen science
Domain: Reproducibility · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearchOpen scienceScholarly communication
Domain: Reproducibility · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.722
metaresearch head score (Gemma)0.682
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.278
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7220.682
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0040.005
Science and technology studies0.0250.195
Scholarly communication0.0260.067
Open science0.0090.030
Research integrity0.0340.038
Insufficient payload (model declined to judge)0.0030.002

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 head0.968
GPT teacher head0.797
Teacher spread0.171 · 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

Labeled directly by 2 models reading the full record.

MetaresearchOpen scienceScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual · Qualitative
DomainReproducibility
GenreEmpirical · Commentary

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

Citations30
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Empirical Research on Human Research EthicsSame topicQualitative Research Methods and EthicsCategoryMetaresearchFrench-language works237,207