The Auckland War Memorial Museum Online Cenotaph: community participation, collective memorialisation and social cohesion
Bibliographic record
Abstract
Purpose Online/Digital cultural heritage platforms have the potential to serve as empowering sites and tools for democratic participation, and for promoting social cohesion, acting as convergence points for diverse societal groups. They enable the gathering of multiple voices, including those of minorities and groups often marginalised in mainstream cultural heritage documentation. This research paper examines the ways in which these aspirations of cultural heritage platforms as meeting, learning and dialogic spaces for connecting and empowering online communities have been realised. Design/methodology/approach Using a qualitative design, interviews were conducted with users of New Zealand’s Auckland War Memorial Museum’s Online Cenotaph. Participants shared their experiences with the platform, perceptions of it as a collective social history resource and views on its role as a participatory space for online communities. They also discussed their expectations for its development as an online space for collective memorialisation. Findings Interviews revealed that users value Online Cenotaph for placing personal, publicly contributed memories and narratives alongside primary military sources. Participants expressed feelings of civic responsibility, social awareness and a sense of identity and connection through their use and contribution to this online commemorative space. The shift from a one-way flow of information from the Museum towards embracing public contribution embodying a high-trust approach, was a notable finding. Originality/value This research underscores the evolving role of museums and other GLAM institutions in recognising the importance of inclusivity, diversity and community participation. It provides insights into how digital cultural heritage social platforms can contribute towards these goals and promote social cohesion. This research is also a starting point for further studies on crowdsourcing and social Web activities on digital cultural heritage platforms as sites of community building through public participation and engagement in historical/cultural heritage narratives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".