Bibliographic record
Abstract
How wonderful! We all write for a multitude of reasons, chief among them is the desire for a competent response. I got exactly that, and I am grateful for this lively dialog. Let me briefly comment to my responders in alphabetical order. My good friend Jere Brophy wisely says we should not think of participation in policy debates as an obligation of all educational psychologists. I would agree, given the way things are now. But I would propose that we rethink the training of educational psychologists so that many of our newly minted doctorates come to see their participation as public intellectuals as an obligation. Lawyers do not have to do pro bono service, but many do because they see it as an obligation. Physicians don’t all join Doctors Without Borders ( Medecins Sans Frontieres ) and risk their lives in war zones, or take a month off each year to work in overseas clinics for room and board, but many do because they see it as their obligation. My concern is that we do not train educational psychologists with the same sense of having a public trust that some attorneys and physicians have. It is not that these two professions are always successful in communicating the obligations of their profession. Clearly the behavior of many lawyers and physicians is not affected by exhortations that public obligations accompany their positions. But they are successful in affecting some members of their profession. We do none of that, and I think we should. Even a little increase in the rate at which educational psychologists take on the role of public intellectuals would please me.
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.338 | 0.242 |
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".