Bibliographic record
Abstract
A sound knowledge of the nature of qualitative research, along with an appreciation of some special ethical considerations, is needed for rigorous reviews to be conducted. The overall character of qualitative research is described with an emphasis on the tendency of qualitative researchers to explore sensitive topics using theoretically informed methods. A number of specific features of qualitative that require additional ethical attention and awareness are also examined including the following: 1) participants are frequently quite vulnerable and require protection because the data collection methods, such as in-depth interviews, can delve into personally and politically charged matters; 2) naturalistic observation can raise concerns regarding privacy and consent; 3) the potential for the identifiability of the results of this research may require extra efforts to maintain confidentiality. Ultimately, Reseach Ethics Committee members must be knowledgeable about qualitative approaches to be able to assess the potential harms and benefits in a protocol carefully. Without this knowledge gaining ethics approval can be overly difficult for researchers and the best practices for protecting human participants can be overlooked.
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 | 0.578 | 0.624 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".