Curating Difficult Knowledge: Violent Pasts in Public Places
Bibliographic record
Abstract
List of Illustrations List of Maps Acknowledgements Notes on Contributors Introduction: Witnesses to Witnessing E.Lehrer & C.E.Milton PART I: BEARING WITNESS BETWEEN MUSEUMS AND COMMUNITIES 'We were so far away': Exhibiting Inuit Oral Histories of Residential Schools H.Igloliorte The Past is a Dangerous Place: the Museum as a Safe Haven V.Szekeres Teaching Tolerance through Objects of Hatred: The Jim Crow Museum of Racist Memorabilia as 'Counter-Museum' M.E.Patterson Politics of the Past: Remembering the Rwandan Genocide at the Kigali Memorial Center A.Sodaro PART II: VISUALIZING THE PAST Living Historically through Photographs in Post-Apartheid South Africa: Reflections on Kliptown Museum, Soweto D.Newbury Showing and Telling: Photography Exhibitions in Israeli Discourses of Dissent T.Katriel Visualizing Apartheid: Re-framing Truth and Reconciliation through Contemporary South African Art E.Mosely PART III: MATERIALITY AND MEMORIAL CHALLENGES Points of No Return: Cultural Heritage and Counter-Memory in Post-Yugoslavia A.Herscher Defacing Memory: (Un)tying Peru's Memory Knots C.E.Milton (Mis)representations of the Jewish Past in Poland's Memoryscapes: Nationalism, Religion and Political Economies of Commemoration S.Kapralski Afterward: The Turn to Pedagogy: a Needed Conversation on the Practice of Curating Difficult Knowledge R.I.Simon Index
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.161 | 0.022 |
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".