Virtue, Objectivity, and the Character of the Education Researcher
Bibliographic record
Abstract
In his 1993 book, Hare asks “What Makes a Good Teacher?” In this paper we ask, “What makes a good education researcher?” We begin our discussion with Richard Rudner's classic 1953 essay, The Scientist Qua Scientist Makes Value Judgments, which confronted science with the internal subjectivity it had long ignored. Rudner's bold claim that scientists do make value judgments as scientists called attention to the very foundations of scientific conduct. In an era of institutional research ethics, like the Tri-Council’s ethics policy, Rudner's call for an approach to these value judgments is even more relevant. The contemporary education researcher primarily engages with ethics procedurally, which provides a certain level of consistency and objectivity. This approach has its roots in principle-based theories of ethics that have long been dominant in Western universities. We argue that calls, like Rudner's, for an objective science of ethics, are at the root of this dominant institutional approach. This paper critiques the suitability of such principle-based ethics for solving Rudner's concerns, and posits that educational research ethics is better understood as a matter of character and virtue. We argue that, much like the ethical teacher, the ethical education researcher is a certain kind of person.
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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 | 0.044 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.123 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".