Bibliographic record
Abstract
Neil Sutherland identifies the 20th-century English-Canadian consensus about childhood. He tracks the beginning of its norms to the turn of the century and studies its growth as an idea shaping teachers’ practices. In pre-service teacher education, Sutherland’s scholarly work helps students learn how teachers "norm" children. Pre-service teachers understand less why certain children fall out of the norm to become abnormal. In this paper, I share how Sutherland maps normal childhood as a timely ideal coinciding with growing social and cultural complexity in Canada. The legacy of childhood as a public policy issue means teacher candidates don’t often question where these ideas began; students see them as timeless. I show how I address this knowledge gap about abnormality in my instruction of Concepts of Childhood in History. In this foundations course, pre-service teachers learn they cannot merely enforce norms but must be critical of them.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".