Sequential Writing Assignments to Critically Evaluate Primary Scientific Literature
Bibliographic record
Abstract
In the later years of undergraduate study, students read, process, and evaluate primary literature within specific fields of study. Shifting from textbooks to the vast amounts of peer-reviewed current literature can be difficult for students. This article details an innovative approach to helping students successfully make this transition through a series of sequential assignments based on the levels of increasing cognitive complexity in Bloom’s taxonomy. These assignments are designed to be flexible enough to use in fields throughout the sciences and beyond, while allowing instructors to tailor these assignments to meet the needs of their particular students. Dans les dernières années de leurs études de premier cycle, les étudiants procèdent à la lecture, à l’assimilation et à l’examen des principaux travaux dans des domaines d’études particuliers. Le passage des manuels d’apprentissage à la masse profuse des travaux de recherche actuels évalués par les pairs peut être difficile pour les étudiants. Dans notre article, nous présentons une approche novatrice visant à aider les étudiants à réussir cette transition grâce à un ensemble de devoirs dont la succession répond à l’échelle de complexité cognitive selon la taxinomie de Bloom. Ces devoirs sont conçus de manière flexible pour un usage dans différents domaines scientifiques et ailleurs, ce qui permet aux enseignants de les adapter selon les besoins particuliers de leurs étudiants.
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 | 0.031 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".