Bibliographic record
Abstract
Presents an obituary for Edward Zeigler (1930-2019). Yale University's Sterling Professor Emeritus of Psychology Edward Zigler often encouraged his students and junior colleagues with the refrain, "You are doing God's work," but warned them that they would have to be ready to "Lose, lose, lose" in the process. This dual-pronged exhortation reflected Ed's value that no cause is greater than that of improving the lives of children and their families who are vulnerable because of life circumstances. For more than half a century, Ed was a tireless and devoted champion for children with intellectual disability, children born into poverty, children from minority backgrounds, adults with psychopathology, and many other marginalized groups. Ed's academic legacy is enshrined by his more than 800 scholarly articles, 43 books and monographs, the numerous social and educational programs that have positively impacted millions of children and families in the United States and around the world, and his generations of students who have contributed to the well-being of children and families in many different ways. This extensive and varied tapestry of accomplishments reflects Ed's world view that success in advocacy, public policy, and social programming is dependent on meticulous science, and that science is only meaningful when it enhances the lives of others. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.072 |
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".