How do you write and present research well? Answers to the 20 questions
Bibliographic record
Abstract
Abstract At the beginning of this series on how to write and present research,[1,2] we repeated Whitesides's[3] message that working in the laboratory, modelling, designing, and troubleshooting constitute only part of the research effort. Discovery, development, analysis, and reviewing literature is work which is incomplete until you publish it and others cite it.[4] Our questions address specifically how to write and present with greater clarity, which is only one element in the process and includes: (a) acknowledging that you should write and present better; (b) deciding that you want to improve; (c) identify means to achieve this goal—courses and books, for example; (d) dedicating time to practice; (e) finding a coach or some way to get feedback on how you are doing; and (f) implementing what you learn in all written and oral communication. Writing and presenting are indispensable skills for researchers, but for many of us, formal instruction on communication ended in high school or the first year of university. However, universities are now implementing soft skill workshops as part of the offering to new graduate students. Funding agencies recognize the importance of these skills and now require programs in grant proposals (NSERC CREATE, European RECHIND). These resources are most effective when students recognize that they need to improve their skills and also want to improve them. Great musicians, athletes, and Go players practice continually. Furthermore, they have coaches to give them feedback and help them develop strategies. Writing is as complex as these activities and, like with them, we can improve at it continually.[5,6]
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 | 0.105 | 0.372 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".