Bibliographic record
Abstract
A common problem in introductory biochemistry courses is the volume of information that must be covered in the standard quarter or semester. This can quickly become overwhelming to the students, who are faced with mountains of information, no way to determine what is important to the professor, and little idea of how to apply this information to problems they may face in other classes or as professionals. I have found that using active learning, primarily in the form of worksheets completed in small groups, very effective at both narrowing the scope of information the students are expected to know and at exposing the students to “problems” that they may face outside the biochemistry classroom where biochemical knowledge will need to be applied. Because of the diverse needs and backgrounds of the students that take this course, I still need to cover a set amount of material in the first semester of biochemistry. I liked the idea of employing active learning in my course; however, because of the amount of content necessary, I could not utilize this teaching style every day. As a result, I have hybridized active learning and lecture to one day of each style per week. This has had the benefit of targeting different learning styles. In narrative evaluations, students have commented that they appreciate both styles, but prefer one or the other. By using both teaching styles, the learning needs of more students are satisfied.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".