The Long and Short of IT: The International Development Research Centre as a Case Study for a Long-term Digital Preservation Strategy
Bibliographic record
Abstract
This thesis is a contribution to the study of the challenges facing archivists and record managers working on the long-term management and preservation of digital records. This thesis discusses the International Development Research Centre (IDRC), a Canadian government Crown agency, as a case study. In 2004 IDRC's Resarch Information Management Service (RIMS) Division was given the responsibility for developing a digital preservation program for the centre's final reports and related documentation. To facilitate this work, it hired a student intern to research recommendations for a digital preservation strategy. My research as the centre's intern led to the following recommendations for IDRC: \nChoose file formats that are ubiquitous, non-proprietary (when possible), viable, and lossless; \nImplement a strategy of conversion and migration of file formats and media as they become obsolete; \nCapture metadata to support the preservation of and access to digital objects; and \nComply with the Open Archival Information System (OAIS) reference model. \nMuch academic study by archivists on digital preservation focuses on the concepts relating to digital records and records management. This thesis offers a practical institutional example of one effort to develop an actual archival program.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.054 | 0.022 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".