Bibliographic record
Abstract
This historical inquiry analyzes the appeal of Harold Rugg’s social reconstructionist social studies for Alberta educators in the 1930s. It demonstrates why and how this small, rural province adapted Rugg’s curriculum, a program and resources he developed to guide American students’ understanding of what he called “the American problem.” It identifies key elements of Rugg’s program, including its philosophical orientation and its practical teaching resources that were particularly appealing to educational leaders. The inquiry identifies legacies of the origins of the program for the provincial social studies curriculum. Keywords: Social studies, Harold Rugg, social reconstructionism, curriculum reform, Alberta Cette enquête historique analyse l'attrait des études sociales reconstructionnistes de Harold Rugg pour les éducateurs de l'Alberta dans les années 1930. Elle démontre pourquoi et comment cette petite province rurale a adapté le curriculum de Rugg, un programme et des ressources qu'il a développés pour aider les élèves américains à comprendre ce qu'il appelait "le problème américain". Elle identifie les éléments clés du programme de Rugg, notamment son orientation philosophique et ses ressources pédagogiques pratiques qui attiraient particulièrement les leadeurs en éducation. L'enquête identifie les retombées des origines du programme sur le programme provincial d'études sociales. Mots clés : études sociales, Harold Rugg, reconstruction sociale, réfonte du curriculum, Alberta
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".