In Defense of Robin Barrow's Concern About Empirical Research in Education
Bibliographic record
Abstract
This paper offers a defense of Robin Barrow’s main arguments in Giving Teaching Back to Teachers, including additional material concerning the inability of the aggregate data and statistical methods employed in research in education (and research on teaching) to speak to individual teachers and students or to particular classrooms. This defense and extension of Barrow’s position is applied in a critique ofa proposal made by Lorraine Foreman-Peck in her 2004 debate with Barrow, entitled What Use is Educational Research?, published in 2005 by the Philosophy of Education Society of Great Britain. A central confusion that attends and limits much empirical research in education and social science concerns conflation of two different senses of the concept general, as “common to all” or “on average.” The havoc this confusion plays ought not be ignored or minimized by educational researchers and their advocates who tend to exaggerate the empirical regularity in social scientific data and therefore the generalizability of social science research in education and elsewhere.
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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 | 0.137 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.120 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.031 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".