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Record W2279277574 · doi:10.1177/1609406915621420

Finding the Hidden Participant

2015· article· en· W2279277574 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSnowball samplingVariety (cybernetics)Nonprobability samplingResource (disambiguation)Process (computing)Qualitative researchParticipant observationVulnerability (computing)PsychologyData scienceSocial psychologyApplied psychologyComputer scienceSociologySocial scienceComputer securityPopulationArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Certain social groups are often difficult for researchers to access because of their social or physical location, vulnerability, or otherwise hidden nature. This unique review article based on both the small body of relevant literature and our own experiences as researchers is meant as a guide for those seeking to include hard-to-reach, hidden, and vulnerable populations in research. We make recommendations for research process starting from early stages of study design to dissemination of study results. Topics covered include participant mistrust of the research process; social, psychological, and physical risks to participation; participant resource constraints; and challenges inherent in nonprobability sampling, snowball sampling, and derived rapport. This article offers broadly accessible solutions for qualitative researchers across social science disciplines attempting to research a variety of different populations.

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.

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)0.015
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.828
GPT teacher head0.685
Teacher spread0.143 · 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