Memorial landscapes and contestation: destabilising artefacts of stability
Bibliographic record
Abstract
Landscapes of memorialisation are, simultaneously, sites of remembering and forgetting. As sites of remembering, memorial landscapes are instructive. Their artefacts of commemoration do not simply recall events and/or people, they extol specific values and lessons that members of their given society are silently urged to aspire to and emulate. However, such landscapes are strategically curated presenting a historical narrative that reflects and supports the dominant socio-political paradigm. Those voices that do not reflect this paradigm are silenced, symbolically excluded and hence forgotten. However, the processes of silencing and forgetting are never absolute. Alternative voices contest dominant memorialisation practices, jostling to be heard in wider societal discourse. The papers in this special issue reflect upon these struggles. Drawing on case studies from across the globe the authors of each paper trace the complexity of and contestation over landscapes of memorialisation. In doing so, this special issue contributes to the multidisciplinary understandings of remembering and forgetting in and through the landscape.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".