Learning to write like a scientist: Coauthoring as an enculturation task
Bibliographic record
Abstract
Abstract This multiple case study examined the coauthorship process in research laboratories of different university departments. The study focused on two cases comprising five writing teams, one in biochemistry and microbiology and four in earth and ocean sciences. The role of the research supervisor, the role of the student (graduate and postgraduate), the interaction of the supervisor and the student, the activities and processes inherent in the coauthorship process, and the student's beliefs, expertise, scientific writing, and entry into an academic discourse community were documented utilizing multiple sources of data and methods. Several activities and processes were found to be common across all coauthorship teams, including aspects of planning, drafting, and revising. Elements of scientific and writing expertise, facets of enculturation into scientific research and discourse communities, academic civility, and the dynamics of collaborative groups also were apparent. There was healthy tension and mutual respect in the research groups as they attempted to make sense of science, report their results clearly and persuasively, and share the responsibilities of expertise. The novice scientists came to appreciate that the writing, editing, and revising process influenced the quality of the science as well as the writing. © 2004 Wiley Periodicals, Inc. J Res Sci Teach 41: 637–668, 2004
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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 | 0.042 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| 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".