Bibliographic record
Abstract
Since the early 2000s, the use of the term presentism has rapidly increased in both the historical discipline and public discussions of history. Most recently, presentism has been widely discussed and debated in articles about the pulling down and defacement of statues in countries around the world inspired by the 2020 Black Lives Matter protests. Many of these discussions reveal a lack of clarity and understanding about presentism’s complex nature. Given how important this concept is to the historical discipline, and how often the term is being used in academic, political, and cultural discourses, we believe presentism warrants further attention and discussion from history educators. This article aims to rethink the place of presentism in history education by considering how historians define and categorize common types of presentism, examining key arguments for and against presentism, and analyzing how history educators have approached it. We conclude by making the case that presentism is a necessary and potentially productive concept for history education.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.122 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".