Embracing the Messiness: A PhD Journey to an Embodied Academic Voice
Bibliographic record
Abstract
To engage in academic work is to step into a space where transitions—between identities, ways of knowing, and academic expectations—are inevitable and necessary. This piece reflects my journey as a PhD candidate, singer, and educator, exploring the quest for an embodied academic voice. It examines how voice—both literal and metaphorical—shapes learning spaces and how performative and embodied knowledge contribute to inclusive academic environments and communities. This work is a revised version of my PhD oral defense, completed in 2023. I share it to inspire other PhD candidates and scholars to explore alternative ways of creating knowledge where embodied ways of knowing are central. Drawing from new materialisms and feminist theory, I argue that academic voices are relational, porous, and in flux rather than static or singular. Through storytelling, I reflect on moments of struggle, discovery, and transformation, engaging in dialogues with both theory and personal experience to encourage holistic and inclusive learning spaces.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".