Bibliographic record
Abstract
Tensions across disciplines and methodologies over what constitutes appropriate academic voice in writing is far from arbitrary and instead is rooted in competing notions of epistemology, representation, and science. In this paper, I examine these tensions as well as address current issues affecting academic voice such as gender bias and the rise of social media. I begin by discussing reflexivity in research and then turn to the ways in which personal-reflexive voice has been hidden and revealed by academic writers. I also illustrate how the commercialization of academic science intersects with the use of distant-authoritative voice in sometimes corrupting ways. I examine variations in academic voice as they relate to issues of researcher emotion, class, race, and gender. Finally, I discuss the scientization of qualitative research and resulting increased interaction between scholars of varying epistemological positions which I argue can increase attention to the epistemological underpinnings of academic voice.
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 | 0.081 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.064 |
| Scholarly communication | 0.037 | 0.023 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".