Bibliographic record
Abstract
Ken Osborne looks back on his career as a history teacher and teacher of history teachers, exploring what first drew him to the study of history, why he thinks history matters, why he decided to teach it, and how he became involved in issues surrounding the teaching of history. Along the way he also comments on the emergence of history education as a distinct field of study and research. Ken Osborne revient sur sa carrière de professeur d’histoire et d’enseignant aux enseignants en histoire, fouillant ses souvenirs sur ce qui l’a d’abord attiré à l’histoire, les raisons pour lesquelles il juge l’histoire importante et pour lesquelles il l’a enseignée, et les circonstances qui l’ont amené à s’intéresser aux enjeux relatifs à l’enseignement de l’histoire. En cour de route, il se penche aussi sur l’émergence du champ d’étude et de recherche distinct de l’enseignement de l’histoire.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.017 |
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; both teacher heads agree on what is shown here.
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".