Scientists' views of science, models of writing, and science writing practices
Bibliographic record
Abstract
Abstract Written and oral communications and the processes of writing and reading are highly valued within the scientific community; scientists who communicate well are successful in gaining recognition and support from members of their own communities, the research funding agencies, and the wider society. Yet how do scientists achieve this proficiency? Are expert scientists equally expert writers in and of science? Do scientists' perceptions of the nature of science influence their writing strategies and processes, and their beliefs about the role of writing in knowledge construction? This study used a questionnaire and semistructured interviews to document these perceptions, strategies, processes, and beliefs in a nonrandom sample of Canadian university scientists and engineers. The results indicate that the scientists subscribed to a contemporary evaluativist view of science, used common writing strategies, held similar beliefs about scientific writing and nonscientific writing, and agreed that writing generates insights and clarifies ambiguity in science. The engineers held a different view of technology than the common views of science or technology as simply applied science. These findings were slightly different than those found for American scientists from a large land‐grant university. © 2004 Wiley Periodicals, Inc. J Res Sci Teach 41: 338–369, 2004
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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".