Educational Psychology as a Policy Science: Thoughts on the Distinction Between a Discipline and a Profession
Bibliographic record
Abstract
Some of you know that I entered the policy arena about a decade ago. After happily and productively working as an educational psychologist in the area of research on teaching, well out of the public eye, I became fed up with the incomplete, distorted and political uses of data that I saw around me. This unhappiness started for me with the publication of A Nation at Risk (National Commission on Excellence in Education, 1983). That influential report described a school system that I didn’t recognize. It had been written by people I knew and liked so I originally kept quiet, thinking that although they may have gone too far, all that attention would be good for education. I believed that the report might help us to get more money for research and that it could lead to policies that would help schools that were not succeeding. But the two years following publication of that inaccurate and data-less screed witnessed the publication of, literally, hundreds of criticisms of the public schools. Eventually it dawned on me that many of the critics were not out to help the schools get better at all. They appeared to me to be out to destroy public education. Whether accurate or not, this realization brought my values to the foreground of my professional life. I discovered that I had a deep, almost visceral commitment to public education. I came to believe that the institution I cherished personally, and believed to be indispensable to our nation, was under attack. I thought I needed to talk about this and so I changed the direction of my career.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.126 |
| Scholarly communication | 0.030 | 0.046 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.029 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 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".