Bibliographic record
Abstract
Abstract This study attempted to establish an image of a science writer based on a synthesis of writing theory, models, and research literature on academic writing in science and other disciplines and to contrast this image with an actual prototypical image of scientists as writers of science. The synthesis was used to develop a questionnaire to assess scientists' writing habits, beliefs, strategies, and perceptions about print‐based language. The questionnaire was administered to 17 scientists from science and applied science departments of a large Midwestern land grant university. Each respondent was interviewed following the completion of the questionnaire with a custom‐designed semistructured protocol to elaborate, probe, and extend their written responses. These data were analyzed in a stepwise fashion using the questionnaire responses to establish tentative assertions about the three major foci (type of writing done, criteria of good science writing, writing strategies used) and the interview responses to verify these assertions. Two illustrative cases (a very experienced, male physical scientist and a less experienced, female applied biological scientist) were used to highlight diversity in the sample. Generally, these 17 scientists are driven by the academy's priority of publishing their research results in refereed, peer‐reviewed journals. They write their research reports in isolation or as a member of a large research team, target their writing to a few journals that they also read regularly, use writing in their teaching and scholarship to inform and persuade science students and other scientists, but do little border crossing into other discourse communities. The prototypical science writer found in this study did not match the image based on a synthesis of the writing literature in that these scientists perceived writing as knowledge telling not knowledge building, their metacognition of written discourse was tacit, and they used a narrow array of genre, strategies, target audiences, and expectations for their writing. © 2002 Wiley Periodicals, Inc.Sci Ed86:672–692, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/sce.10042
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".