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Record W3014788491 · doi:10.1037/amp0000506

Edward Zigler (1930–2019).

2020· article· en· W3014788491 on OpenAlexaff
Jacob A. Burack, Suniya S. Luthar

Bibliographic record

VenueAmerican Psychologist · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsChampionPsycINFOPovertyPsychologyObituarySocial workDisadvantagedNature versus nurtureValue (mathematics)SociologyPolitical scienceMEDLINELaw

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.021

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.

Opus teacher head0.077
GPT teacher head0.406
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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