Bibliographic record
Abstract
Case studies are used to guide students’ natural curiosity-driven learning instead of traditional content-heavy lectures. In collaboration with Dr. Marta Cerruti and one other co-teacher, I developed case studies for the undergraduate pre-requisite course “Analytical and Characterization Techniques” (MIME 317) to teach the material characterization concepts such as Atomic Absorption or UV/Vis spectroscopy in case-study driven manner. The process included understanding the professors’ desired learning outcomes and finding journal articles that used such concepts to solve real-world problems. Then, I developed handouts to simplify the complicated concepts presented in the articles and crafted questions that students with no background knowledge could still answer given the information provided and the figure/graph from the article. Finally, in delivering the case studies in class, I facilitated group discussion and found that guiding the discussion based on the students’ curiosity deepened their understanding of the subject.
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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".