Bibliographic record
Abstract
Using an innovative reading assignment structure balances the benefits of studying real research — not just a textbook — against the reality of too little time. Each student reads original research that uses at least one of the research methods studied. With these same studies used as in-class examples, students learn how the assumptions underlying research questions affect research methods chosen, how researchers using different methods create, present, and evaluate evidence, and how methods shape research findings. The final test requires students to use the study they have read to examine these themes. In one-semester courses, particularly where semesters are short, requiring that students both read a textbook and original research is often impractical. This is especially true in a research methods course where each textbook chapter is filled with new vocabulary and concepts that students must invest time to learn. Few students have the time, and perhaps fewer have the will, to read an additional 50 pages every week once their textbook reading and studying is done. Nonetheless, a methods course is more than a course in methodological vocabulary. In methods courses students should also learn that methods are intertwined with every aspect of the research process and that evaluating evidence requires awareness of these connections. This is best learned by examining how these connections play out in actual research projects. The syllabus provides an example reading for each method studied. These are termed elective readings: while no specific reading is required, students must do at least one reading on the list. Students can manage their own workloads over the course of the semester by choosing the week for which they will do an extra reading. Students’ varying schedules and preferences ensure that each week there are some students in class who have done the reading for that week and can contribute to a discussion of this example.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".