Conflicts, battlefields, indigenous peoples and tourism: addressing dissonant heritage in warfare tourism in Australia and North America in the twenty‐first century
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the omission of Indigenous narratives in battlefields and sites of conflicts while also highlighting how certain battlefields and sites of conflicts have attempted to address dissonant heritage by diversifying interpretation strategies and implementing elements of collaborative management approaches, thereby addressing Indigenous erasure. Design/methodology/approach The study uses a content analysis, field studies and case studies to examine dissonant heritage in warfare tourism sites involving Indigenous peoples in Australia and North America. Findings The content analysis reveals that aboriginal erasure is still prevalent within the literature on warfare and battlefield tourism. However, the case studies suggest that dissonant heritage in warfare tourism is being addressed through collaborative management strategies and culturally sensitive interpretation strategies. Research limitations/implications The content analysis is limited to tourism journals. The case studies highlight sites that are using adaptive management and integrating Indigenous peoples. Practical implications The study of dissonant heritage and warfare tourism, while relatively young, is beginning to address aboriginal erasure and cultural dissonance; this study is a contribution to this area of research. Social implications Addressing the impacts of aboriginal erasure and heritage dissonance in colonial settings heals the hurts of the past, while empowering communities. It also provides Indigenous communities with opportunities to diversify current tourism products. Originality/value This is a collaborative international paper involving Indigenous and non‐Indigenous scholars from Australia, Canada, and the USA.
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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 | 0.003 | 0.003 |
| 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 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".