Integrating Positionality Statements in Empirical Software Engineering Research
Bibliographic record
Abstract
Context. Positionality statements are a reflective practice that is well-established in fields such as social sciences, where they enhance transparency, reflexivity, and ethical integrity by acknowledging how researchers' identities, experiences, and perspectives may shape their work. Goal. This study aimed to investigate the understanding, usage, and potential value of positionality statements in software engineering (SE) research, particularly in studies focused on diversity and inclusion (D&I). Method. We conducted a qualitative survey targeting authors of D&I-focused studies in SE to explore their perspectives and practices regarding positionality. Through purposive sampling, we collected responses from 21 participants, which were analyzed using thematic analysis to identify how positionality is currently understood and applied. Findings. Our findings reveal that SE researchers often view positionality statements as a method for self-reflection, contextual awareness, and bias reduction, though practices vary widely. While some participants explicitly integrate positionality statements into their research, most apply these concepts implicitly. Challenges, such as double-anonymity requirements and the perception of objectivity in SE, also limit the adoption of positionality. Discussions. Our findings highlight an opportunity for SE to adopt and adapt positionality statements to reflect the field's intersection of technical and human considerations. By incorporating structured positionality practices, SE research could enhance inclusion, ethical rigor, and transparency, moving closer to the standards established in more mature disciplines. Conclusion. Although SE research increasingly addresses complex social and human-centered issues, positionality statements have yet to become common practice in the field.
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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 | 0.336 | 0.484 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".