OSS4EVA: Using Open-Source Tools to Fulfill Digital Preservation Requirements
Bibliographic record
Abstract
This paper builds on the findings of a workshop held at the 2015 International Conference on Digital Preservation (iPRES), entitled, “Using Open-Source Tools to Fulfill Digital Preservation Requirements” (OSS4PRES hereafter). This day-long workshop brought together participants from across the library and archives community, including practitioners, proprietary vendors, and representatives from open-source projects. The resulting conversations were surprisingly revealing: while OSS’ significance within the preservation landscape was made clear, participants noted that there are a number of roadblocks that discourage or altogether prevent its use in many organizations. Overcoming these challenges will be necessary to further widespread, sustainable OSS adoption within the digital preservation community. This article will mine the rich discussions that took place at OSS4PRES to (1) summarize the workshop’s key themes and major points of debate, (2) provide a comprehensive analysis of the opportunities, gaps, and challenges that using OSS entails at a philosophical, institutional, and individual level, and (3) offer a tangible set of recommendations for future work designed to broaden community engagement and enhance the sustainability of open source initiatives, drawing on both participants’ experience as well as additional research.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".