Practical history lessons as a tool for generating procedural knowledge in history teaching
Bibliographic record
Abstract
Situated in the context of philosophy of history, this article explains the use of substantive concepts and procedural concepts to generate historical understanding and examines the relationship between the two forms of historical knowledge. The paper makes use of both primary (original views of authors) and secondary (views of other authors) materials. The paper notes that substantive knowledge and procedural knowledge play complementary roles in the acquisition of historical understanding. It is argued, in light of the dominant position of substantive knowledge over procedural knowledge, that attention should be given to procedural knowledge as it introduces students to the processes by which history is constructed. The article proposes the use of practical history lessons as a conduit for developing procedural knowledge and attaining historical understanding.Situated in the context of philosophy of history, this article explains the use of substantive concepts and procedural concepts to generate historical understanding and examines the relationship between the two forms of historical knowledge. The paper makes use of both primary (original views of authors) and secondary (views of other authors) materials. The paper notes that substantive knowledge and procedural knowledge play complementary roles in the acquisition of historical understanding. It is argued, in light of the dominant position of substantive knowledge over procedural knowledge, that attention should be given to procedural knowledge as it introduces students to the processes by which history is constructed. The article proposes the use of practical history lessons as a conduit for developing procedural knowledge and attaining historical understanding.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".