Bibliographic record
Abstract
Educational research is contested terrain, too often succumbing to narrow conceptions of what constitutes legitimate ways of knowing. What does it mean to be “scientific” when doing educational research? What counts as data? Can books count as a “data set” (St. Pierre, 2013)? Most graduate students in education are introduced to educational research in research methodology courses, most of which include required textbooks on qualitative and quantitative methods. Most research methodology textbooks obscure the notion that there is anything other than empirical research. As one example, perhaps the most dominant methods textbook is Creswell’s (2018) Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, which states that “A research method is a specific and detailed procedure for answering research questions. These methods can be grouped into qualitative and quantitative approaches” (p. 3).
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 | 0.112 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.011 | 0.131 |
| Scholarly communication | 0.030 | 0.050 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".