10. Inspiring Writing in the Sciences: An Undergraduate Electronic Journal Project
Bibliographic record
Abstract
Most faculty will agree that students must learn to write well (Emerson, MacKay, MacKay, & Funnell, 2006), and in the sciences, a variety of approaches have been taken. In the College of Physical and Engineering Science at the University of Guelph, we have developed a way of embedding research, writing, and analytical skills into an introductory Nanoscience course that gives students the true-to-life experience of writing for publication, ignites their imaginations, and inspires them to do their best. Following the process of scholarly publication, students become researchers, authors, and reviewers for an electronic journal. Through appropriately timed workshops and tutorials, they receive support and feedback. Rubrics for the assessment of the students’ performances as authors and peer reviewers provide them with more insight into what constitutes work that falls below expectations, or meets or exceeds them. These rubrics also enable faculty to evaluate student contributions efficiently and fairly. In this essay, we showcase a suite of pedagogical tools that includes learning activities, open access software and assessment rubrics, and share our experiences of a faculty-librarian collaboration.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".