Social Identity Map: A Reflexivity Tool for Practicing Explicit Positionality in Critical Qualitative Research
Bibliographic record
Abstract
The way that we as researchers view and interpret our social worlds is impacted by where, when, and how we are socially located and in what society. The position from which we see the world around us impacts our research interests, how we approach the research and participants, the questions we ask, and how we interpret the data. In this article, we argue that it is not a straightforward or easy task to conceptualize and practice positionality. We have developed a Social Identity Map that researchers can use to explicitly identify and reflect on their social identity to address the difficulty that many novice critical qualitative researchers experience when trying to conceptualize their social identities and positionality. The Social Identity Map is not meant to be used as a rigid tool but rather as a flexible starting point to guide researchers to reflect and be reflexive about their social location. The map involves three tiers: the identification of social identities (Tier 1), how these positions impact our life (Tier 2), and details that may be tied to the particularities of our social identity (Tier 3). With the use of this map as a guide, we aim for researchers to be able to better identify and understand their social locations and how they may pose challenges and aspects of ease within the qualitative research process. Being explicit about our social identities allows us (as researchers) to produce reflexive research and give our readers the tools to recognize how we produced the data. Being reflexive about our social identities, particularly in comparison to the social position of our participants, helps us better understand the power relations imbued in our research, further providing an opportunity to be reflexive about how to address this in a responsible and respectful way.
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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 | 0.220 | 0.359 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".