Negotiating the Insider/Outsider Researcher Position within Qualitative Disability Studies Research
Bibliographic record
Abstract
The subjectivity of qualitative researchers can be a contribution to qualitative research which at the same time requires commitment to on-going critical reflexivity regarding one’s positionality. More specifically, we address how to navigate the possibility that researcher subjectivity can culminate in role-confusion when the researcher is highly familiar with the research setting or research participants, when positioned as an “insider.” We do this by adopting a critical paradigm approach that investigates the efficacy of “unlearning” as a strategy for challenging one’s assumptions as a researcher, particularly those assumptions that challenge the co-construction of knowledge that extends from research presuppositions. Drawing upon theoretical and methodological literature, we argue that intersubjective reflection is crucial to the process of unlearning. By critically reflecting on subjectivity, it becomes possible to deconstruct our research approach and its underlying assumptions, as well as our research findings. In turn, this creates space to unpack our role in how these approaches, assumptions, and findings are formulated, as well as space to challenge and reformulate these based on dialogue with participants. Through critical reflexivity addressing subjectivity and positionality in the context of research relations, researchers are challenged to consider how their insider knowledge, based on their individual experiences and personal meanings, can impinge on 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 | 0.564 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.023 | 0.116 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".