Philosophy of Education and the Contested Nature of Empirical Research: A Rejoinder to D.C. Phillips
Bibliographic record
Abstract
INTRODUCTION In a recent article published in the Journal of the Philosophy of Education, D.C. Phillips makes a valiant if ultimately unsuccessful attempt to rescue empirical research in education from a range of terminal defects. With tongue in cheek, Phillips employs such weighty experts as Woody Allen and Sir Arthur Conan Doyle — we presume intentionally committing the fallacy of appeal to erroneous authority — to support his mission. In the final analysis, however, Phillips’s wittily crafted apology for the dominant research paradigm in education unfortunately misrepresents important philosophical critiques on the limits of empirical research. In this essay, we challenge Phillips’s defense of empirical research in education and argue that his attack on Kieran Egan in particular fails to address the considerable force of the latter’s most contemporary critique. From the outset of this rejoinder, we also wish to convey our tremendous professional respect for Phillips and his many contributions to the philosophy of education.
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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 | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.085 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.001 | 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".