Book Review: Contested Histories in Public Space: Memory, Race, and Nation edited by Daniel J. Walkowitz and Lisa Maya Knauer
Bibliographic record
Abstract
Book Review 52 Contested Histories in Public Space: Memory, Race, and Nation Edited by Daniel J. Walkowitz and Lisa Maya Knauer Duke University Press (2009) ISBN: 0822342367, 376 pages. Reviewed by: BROOKE NEELY How are racial and national narratives imagined in a postcolonial, global context? A growing body of work across the disciplines locates these processes in public sites of commemoration. Memorials, monuments, and other historical spaces operate as contested terrains for constructing, opposing, and negotiating racial and national discourses. In Daniel J. Walkowitz and Lisa Maya Knauer’s edited volume Contested Histories in Public Space: Memory, Race, and Nation, contributors illustrate these representational struggles at a variety of historical sites around the world, from New Zealand’s national museum to South Africa’s Voortrekker Monument, highlighting in particular how postcolonial theory has informed the character and politics of public debates over how to convey history. Walkowitz and Knauer argue that this is a particularly important time to study debates over history in the public sphere because deindustrialization and outsourcing have made cultural and historical sites important spaces for economic development. To address the range of ways public histories get contested, they divide the book into four main thematic sections that take the reader on a tour of four theoretical problems the editors see as central to the study of memory, race, and nation. The first section focuses on how indigenous groups engage with national historical sites in Australia, New Zealand, and Canada and attempt to expand and complicate the national narratives that circulate in these memorial spaces. For example, Ruth B. Phillips and Mark Salber Phillips offer a compelling piece on the First Peoples’ Hall at the Canadian Museum of Civilization. They detail the conflicts and collaborations that emerged in the process of expanding the museum’s narratives in ways that legitimate indigenous epistemologies and foreground First Peoples’ traditional and contemporary experiences. In this case, we see how museum spaces can avoid singular narratives and artifact-oriented displays by offering unfinished, multiple, and fragmented stories of current and former Native inhabitants as well as by conveying the ongoing political struggles surrounding identity, land rights, and sovereignty. Similarly, Paul Ashton and
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".