My journey through my Qualifying Exam using reflexivity and resonant text: ‘what I know’; ‘how I know it’; and ‘how I experience it’
Bibliographic record
Abstract
The Qualifying Examination (QE) is an important but, at the same time, isolated journey for doctoral students. Along with many other doctorate students in Public Health Sciences, I was provided with a description of the core requirements of my QE and left alone in a solitary journey of readings and writings. Reflexivity, the act of becoming aware of the self as author/researcher, and resonant text, the use of art as an expressive medium for personal learning, were not required during my QE. However, as a scholar, my ontological and epistemological knowledge is grounded in social‐critical/feminists paradigms, which encourage the use of reflexive practices to locate the researcher position during the research process. As a result, I felt the need to explore and disclose my research and personal identities during my QE process. Furthermore, I wanted to explore different forms of communicative mediums (e.g. art‐based/visual medium) to learn and disseminate knowledge. Using my personal experience, this paper tries to answer the question: How can reflexivity and resonant text help doctoral students to explore and understand their multiples selves during their QE? Following a feminist reflexive framework, I identified my multiple selves in relation to the literature that I reviewed. Furthermore, I created four resonant texts: Self; Sea of Text; Castle of Knowledge; and Lived History of Body and Gender. In reflecting on my QE journey, I am now aware of how my resonant texts become my way to re‐express ‘what I know’, ‘how I know it’, and ‘how I experience it’.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.011 |
| 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; 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".