Reflexive Accounts: An Intersectional Approach to Exploring the Fluidity of Insider/Outsider Status and the Researcher’s Impact on Culturally Sensitive Post-Positivist Qualitative Research
Bibliographic record
Abstract
Reflexivity and acknowledging the role of the researcher in the research is a well-established practice in post-positivist research. In this paper we use reflexivity to examine our personal experiences in conducting qualitative research. We use reflexivity to understand how our intersecting identities and resulting insider/outsider status may have influenced the data collection phase of a study regarding the culturally and religiously sensitive issue of male-female intimate relationships. Using an intersectional approach, we explore the fluidity of our insider/outsider statuses resulting from our multiple and intersecting identities such as ethnicity, religion, age, and sex. The multiple identities a researcher possesses can cause him/her to be perceived as an insider and outsider simultaneously, which can play a significant role in shaping the interactions between the interviewer and interviewee. We present reflexive accounts on how our identities may have affected the data collection process and participants’ comfort level when discussing sensitive issues, in this case sexuality. Overall, we seek to provide insight into the role of intersecting multiple identities and the resulting insider/outsider status in qualitative data collection when examining culturally and religiously sensitive issues from the perspective of the researchers.
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 | 0.201 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.017 | 0.075 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".