Aristote dans l’Europe des XVIe et XVIIe siècles : transmissions et ruptures. Actes du colloque international organisé à Nancy (5, 6 et 7 novembre 2015)
Bibliographic record
Abstract
comptes rendus 233 designs a series of research-based exercises that encourage independent investigation with tools and texts especially suited to history plays.His work is also unique in this collection for its data-supported measures of success.That not all the essays in this volume combine active learning strategies with genre-focused study points to a continuing challenge with these plays: how to tap into what is compelling about Shakespeare's histories-as histories-for those who will have neither time nor inclination to absorb the context.I suspect, if the association publishing this series were British instead of American, the range of strategies to achieve this goal might look different.Perhaps we can take a page from British cultural materialist and presentist understandings of the histories in performance and tap into interests in our own origin stories.Caroline McManus's essay on teacher training tends in this direction by linking Common Core privileging of "foundational US documents" (187) to active learning strategies for historiographic investigation.US "histories" compete, as Hamilton's success attests.As an early critic of the musical observed, the story of one individual is transformed to a story of a nation created by immigrants.Our North American fascination with business leaders and the histories of their companies is another potentially exploitable connection.Approaches to teaching the Roman Plays, anyone?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".