Preservation as access: Digital preservation evaluation and the pre-ingest workflow at Library and Archives Canada
Bibliographic record
Abstract
Digital preservation evaluation is central to Library and Archives Canada’s (LAC) strategic objective of enabling greater access to its collections. While all of LAC’s digital processing and preservation functions support and enable access, pre-ingest, a preservation evaluation workflow that entails the staging and review of transferred digital records — allows LAC to proactively identify preservation issues and is central to navigating and mitigating downstream access difficulties. This paper situates pre-ingest within the policy context of LAC and the Government of Canada, describes the main tasks of the pre-ingest workflow, and discusses how and why these activities are important for both long-term preservation and access. Specifically, it details the main tasks of pre-ingest analysis and triage, as well as the adoption of LAC’s Local Digital Format Registry tool, which automates the application of LAC’s policy registry on file formats to potential acquisitions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".