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Record W2792336088 · doi:10.32316/hse/rhe.v30i1.4607

David Wright, SickKids: The History of the Hospital for Sick Children

2018· article· en· W2792336088 on OpenAlexvenueno aff
Judith Young

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsWrightSick childHistoryMedicineArt historyPediatrics

Abstract

fetched live from OpenAlex

public schools has identified systemic inequalities of outcome based on class, race, ethnicity, and mother tongue.To the consternation of their community, Portuguese students have registered particularly adverse outcomes related to streaming, graduation rates, and post-secondary school attendance.What steps Clinton took to ameliorate their educational prospects, is of interest, though beyond the architecture of this superb book.Robert Vipond declares at the outset that this was not a book he had any intention of writing.It flowed neither from his previous work, nor from any robust current of his academic discipline.Instead, the project found him.The result is a most thoughtful, engaging school history -full of empirical, analytical, and theoretical insight into how a school should be in a diverse, divided city and world.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.011
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.087
GPT teacher head0.298
Teacher spread0.211 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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