“You’re Filipino, you know what I mean”: navigating co-researcher roles, relational graduate supervision, and shared reflexivity
Bibliographic record
Abstract
Reflexivity through shared practices like journaling and ongoing conversations proved to be fruitful for understanding and navigating our positionalities throughout a research study that sought to understand leisure and identity and experiences of new settlers of Philippine descent in times of un/underemployment. Drawing from experiences and lessons learned from engaging in this research, we consider: multiple mediating co-researcher roles that emerged over the course of this project; experiences navigating a sense of comfort over a shared identity with research participants; and our efforts to navigate considerations associated with graduate supervision centered on care. Ultimately, we reflect on and share the ways in which a shared form of reflexivity and relational mentorship help to strengthen the research process and urge scholars to mindfully reflect on, acknowledge, and attend to their own positionalities to cultivate relationships that are supportive, care-full, and relational.
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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 | 0.062 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".