Archiving First Nations Media: the race to save community media and cultural collections
Bibliographic record
Abstract
Since the 1970s First Nations media organisations have been established across remote, regional and urban Australia, and have been broadcasting and producing media in and for their local communities. Many of the resulting community-managed audiovisual collections have yet to be digitised or archived and are often stored in substandard conditions. With UNESCO's deadline of 2025 for digitisation of analogue media rapidly approaching, these rich social and cultural heritage collections are at high risk of being lost. Since 2013 First Nations Media Australia (FNMA, formerly Indigenous Remote Communications Association) has worked closely with member organisations and national collection agencies to develop a First Nations Media Archiving Strategy and to support community organisations develop the capacity to manage their collections according to best practice. FNMA is committed to keeping strong community control of media collections and recordings, and believes that the relationship between media production and access to archived recordings is intrinsically linked to the processes of self-determination and to social, cultural and economic sustainability and benefit. This paper explores the ways in which on-country archiving work enables local decision-making processes, which are considered critical to future collection access and use. The paper discusses how First Nations media organisations are often hampered by a lack of funding for the equipment, software and training needed for preservation work and ongoing management of community collections.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".