Goodbye Colston, goodbye Columbus: Why we need to learn history in times of memorial controversies?
Bibliographic record
Abstract
Abstract Around the western world various activist groups confront controversial monuments and other mnemonic infrastructures of historical culture representing contested histories and equally contested visions of the future. This article presents an original model for analyzing controversial issues of commemoration in the context of history education. Relying on the theory of historical consciousness, it first presents monuments as a distinctive type of mnemonic infrastructures of historical culture. It then delineates a conceptual model for making sense of various “types” or ways of engaging with these infrastructures: preservational, analytical, hypercritical, and reflexive. These ways of engaging are then analyzed in reference to four competencies of historical consciousness in relation to Jörn Rüsen’s recognized typology (inquiry, historical thinking, orientation, and narrative). The article explains how this new model can be transposed to the context of education so as to help students analyze past and current memorial controversies and ultimately develop more complex ways of engaging with mnemonic infrastructure in society.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 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".