Collaborating to Teach Research Methods in Education
Bibliographic record
Abstract
The purpose of this paper is to describe a pedagogical collaboration between two research methods instructors in a Faculty of Education in Canada. Both instructors represent different paradigms in the classic quantitative vs. qualitative dichotomy in that they were trained in vastly different ways and have tended to approach their research along these same lines. However, despite these differences the paper explores how they each viewed this as a potential limitation in their methods teaching and how through crossing over to each other’s classrooms were able to both expand their own understanding as well as offer a more balanced and useful learning experience for the learners in their classrooms.
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 | 0.211 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".