Bibliographic record
Abstract
Dynamic and proactive archives are crucial for safeguarding and growing community memory and knowledge. Despite this, South Pacific Island archives are plagued by stark challenges which hinder their role. Principally, it is an issue of trust. The echoes and expectations of a not-too-distant colonial past have isolated archives from the communities they are supposed to serve. This is made worse by traditional archival practice, which has a narrow focus, and with characteristics and requirements that prevent Pacific archives from connecting with their communities. These dated archive practices concentrate on ‘control’ of archival holdings with less consideration for the ‘accessibility’ of these holdings to the general public. This is driven by assumptions that may be relevant in Europe and societies where the written record has a long history, but which do not fit the realities of the island nations of the South Pacific and other countries that are former colonies, where oral tradition has a more dominant role. Using the developments at the National Archives of Fiji from 2012 to 2019 as a case study, this chapter will examine the challenges to Pacific Island archives, reveal how acknowledging cultural norms is key for Pacific archives to build trust and establish relevance in the community, and demonstrate how connecting with community is critical to overcoming the obstacles which prevent archives from serving their communities as desired.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".