The writer's voice repertoire: Exploring how health researchers accomplish a distinctive ‘voice’ in their writing
Bibliographic record
Abstract
INTRODUCTION: Much published research writing is dull and dry at best, impenetrable and off-putting at worst. This state of affairs both frustrates readers and impedes research uptake. Scientific conventions of objectivity and neutrality contribute to the problem, implying that 'good' research writing should have no discernible authorial 'voice'. Yet some research writers have a distinctive voice in their work that may contribute to their scholarly influence. In this study, we explore this notion of voice, examining what strong research writers aim for with their voice and what strategies they use. METHODS: Using a combination of purposive, snowball and theoretical sampling, we recruited 21 scholars working in health professions education or adjacent health research fields, representing varied career stages, research paradigms and geographical locations. We interviewed participants about their approaches to writing and asked each to provide one to three illustrative publications. Iterative data collection and analysis followed constructivist grounded theory principles. We analysed interview transcripts thematically and examined publications for evidence of the writers' described approaches. RESULTS: Participants shared goals of a voice that was clear and logical, and that engaged readers and held their attention. They accomplished these goals using approaches both conventional and unconventional. Conventional approaches included attention to coherence through signposting, symmetry and metacommentary. Unconventional approaches included using language that was evocative (metaphor, imagery), provocative (pointed critique), plainspoken ('non-academic' phrasing), playful (including humour) and lyrical (attending to cadence and sound). Unconventional elements were more prominent in non-standard genres (e.g. commentaries), but also appeared in empiric papers. DISCUSSION: What readers interpret as 'voice' reflects strategic use of a repertoire of writing techniques. Conventional techniques, used expertly, can make for compelling reading, but strong writers also draw on unconventional strategies. A broadened writing repertoire might assist health professions education research writers in effectively communicating their work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".