Bibliographic record
Abstract
David Berliner presents a compelling argument for the engagement of educational psychologists in policy research, analysis, argumentation, and commentary. He sees this activity as a professional obligation of senior educational psychologists. Providing examples of educational psychology constructs and research that could contribute to policy debates, he suggests that there are warranted truths to be told to policy makers that could affect educational policies and eventually impact the education of our students. Of course, he also states that warranted truths (is rather than ought data) may not necessarily have much of an impact on policy. Thus it is worth looking closely at what Berliner is asking of senior educational psychologists. Throughout the paper, Berliner calls for the following activities: policy analysis, speaking the truth to power, using or creating data that will help to clarify an issue, developing arguments to clarify an issue, entering policy debates and challenging proposed (or existing) policies, publicly professing, and conducting studies with policy implications. As I understand his view on this topic, he sees most educational psychologists as engaging in what they consider to be value-free, objective research, whereas he is calling for what he labels While he feels that partisan research can be reasonably objective, it is not value free. We can think of this characterization of educational psychological research as lying along a continuum from perceived-as-value-free and objective research to value-laden partisan research. He is asking that more effort be devoted towards partisan research. In the next sections of this response, I will address 1) values in research, 2) a friendly critique of partisan research, and 3) a suggestion for the development of guidelines for good partisan research.
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 | 0.088 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 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".