Emerging researcher pedagogies: The “Dear Data” project
Bibliographic record
Abstract
This special edition of the Morning Watch is the second collection of papers from the Faculty of Education doctoral students who are participating in ED 702 A/B Advanced Research Methodology in Education in 2017/18. ED 702 is a core course and is delivered over two semesters. This year we used Patricia Leavy’s (2017) book Research Design to anchor our discussions. Through this book we discussed quantitative, qualitative, arts-based, and community-based participatory research approaches. In this course, and other courses, students become familiar with the ins and outs of research methodologies as they search for the methodology, or even methodologies, they will focus on in their own research projects. In addition to the theoretical knowledge of research methodologies, we, the course facilitators, wanted to include further experiences in our pedagogy. For us, creativity was something we felt was important and often under-represented in research courses. We also wanted to link creativity to critical thinking in students’ minds. Practical research knowledge was also a priority. All of these are difficult to include in a seminar-based course. Creativity is a complex, multifacted concept and is often not linked to critical thinking, and practical research knowledge is challenging to impart in a theoretical course. How can students experience the day-to-day logistics of a research project including unexpected challenges without actually undertaking a research project?
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 | 0.081 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".