Finding the Hidden Participant
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".