Bibliographic record
Abstract
EDITOR'S NOTE: This contribution to our Perspectives section is an unusual yet significant one that celebrates a exceptional pair of educators and at the same time explores deep insights into education. It is a speech, given at the University of South Carolina, that honours an Emeritus Professor of the McGill Faculty of Education, Professor Eigil Pedersen. At the same time it pays tribute to an extraordinary Grade One teacher, Miss A. who was the subject of a famous study by Professor Pedersen. We are delighted to have been given permission by Daniel Fallon of the Carnegie Corporation in the United States to publish his speech in this form. NOTE: Cette contribution a la section Perspectives de notre revue est inhabituelle mais importante. Elle rend, en effet, hommage a la fois a deux educateurs et nous fait jeter un regard profond sur l'education. Il s'agit d'un discours en l'honneur d'Eigil Pedersen, professeur emerite de la Faculte des Sciences de l'education de l'Universite McGill. On y celebre une enseignante exceptionnelle de 1ere annee, Mlle A., qui fut un sujet d'etude du Professeur Pedersen. Nous sommes ravis d'avoir recu de Daniel Fallon de la Carnegie Corporation aux Etats-Unis la permission de la publier sous cette forme.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".