Bibliographic record
Abstract
The 11-minute VR film Border Sounds takes the viewer on a journey across an invisible line that separates Northern Ireland from the Republic of Ireland through haikus and sounds by people who live near this line. These stories were captured during a collaborative outreach program by the official archive for Northern Ireland, PRONI (Public Record Office of Northern Ireland), and the creative media hub Nerve Centre in 2021. This reflective practice-led research examines the filmmaking process, particularly how digital technology was used to engage with people remotely and make a film in a participatory way: to what extent can digital technology, such as Zoom, make filmmaking more accessible to rural people? What role can participatory filmmaking play in post-conflict storytelling? I also discuss the strengths and limitations of virtual reality as a storytelling format – does it really offer a more immersive experience? Are there any access barriers? Is it the most adequate medium to tell stories about the border, particularly in sensitive contexts such as Northern Ireland? I argue here that while virtual reality technology enabled Border Sounds to offer a unique on-screen experience of the Irish Border, it also has limitations in terms of audience reach, long-term preservation and access.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.693 | 0.415 |
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".