Bibliographic record
Abstract
Academic voice is an oft-discussed, yet variably defined concept, and confusion exists over its meaning, evaluation, and interpretation. This paper will explore perspectives on academic voice and counterarguments to the positivist origins of objectivity in academic writing. While many epistemological and methodological perspectives exist, the feminist literature on voice is explored here as the contrary position. From the feminist perspective, voice is a socially constructed concept that cannot be separated from the experiences, emotions, and identity of the writer and, thus, constitutes a reflection of an author's way of knowing. A case study of how author presence can enhance meaning in text is included. Subjective experience is imperative to a practice involving human interaction. Nursing practice, our intimate involvement in patient's lives, and the nature of our research are not value free. A view is presented that a visible presence of an author in academic writing is relevant to the nursing discipline. The continued valuing of an objective, colorless academic voice has consequences for student writers and the faculty who teach them. Thus, a strategically used multivoiced writing style is warranted.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".