Bibliographic record
Abstract
Qualitative researchers are expected to engage in reflexivity, whereby they consider the impact of their own social locations and biases on the research process. Part of this practice involves the consideration of boundaries between the researcher and the participant, including the extent to which the researcher may be considered an insider or an outsider with respect to the area of study. This article explores the three different processes by which boundaries are made and deconstructed, and the ethical complexities of this boundary making/(un)making process. This paper examines the strengths and limitations of three specific scenarios: 1) when the researcher is fully cloaked and hiding their positionalities; 2) when there is strategic undressing to reveal some positionalities; 3) when there is no cloak, and all positionalities are shared or revealed. This paper argues that it is insufficient to be reflexive about boundaries through acknowledgement, and instead advocates reflexivity that directly examines the processes by which social locations are shared and hidden during the research process.
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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 | 0.457 | 0.539 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.026 | 0.154 |
| Scholarly communication | 0.031 | 0.041 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".