Difficult Public History and National Identity in Aotearoa New Zealand: A Narrative Approach to Museum Analysis
Bibliographic record
Abstract
Abstract In settler colonies such as the United States, Canada, Australia, and Aotearoa New Zealand, “memory wars” have, driven by conflicting narratives about colonial history, intensified in recent years. Indigenous counter-narratives challenge Euro-centric master narratives, particularly in public spaces such as museums and monuments. This article explores the impact of such conflicts on national identity, focusing on Aotearoa New Zealand, where the history of colonization has long been framed as a relatively benign process, underpinned by the Treaty of Waitangi. Through application of a comprehensive narratological framework, the article reveals how the Waitangi Treaty Grounds’ permanent exhibition, Ko Waitangi Tēnei: This is Waitangi , employs the quest masterplot to weave Māori memories of the mid-1800s New Zealand Wars into the national master narrative. The analysis highlights that this narrative emplotment – by “naturalizing” the events of the New Zealand Wars – serves to elide difficult questions about colonial violence, thus protecting the image of a tolerant and respectful nation. More generally, the article contributes to our understanding of national identity construction in the context of difficult histories, while also advancing theoretical approaches to narratology in museum storytelling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".