209.2 Centralize it! : Creating infrastructure to support digital preservation. (Part 2)
Bibliographic record
Abstract
âThis panel will discuss the challenges and advantages of establishing centralized digital preservation intake services at large and distributed institutions. Each panelist will present from a different stage of implementation, offering distinct perspectives on the process, followed by a moderated discussion and welcomed questions from the audience. Jess Whyte, from University of Toronto, will discuss the process of conducting a needs assessment and planning for a pilot project. Alice Prael will present the findings from a pilot centralized service for special collections at Yale University and explore the shift from a pilot project to a sustainable program. Dinah Handel will discuss the service management approach as it relates to digitization and digital preservation, as well has how Stanford has centralized their Digitization Services within the Digital Library Systems and Services department to meet stakeholder and inter-department needs. The goal of the session is to develop the discourse around infrastructure, the realities of implementation, and how the paths to maturity are not always linear or singular. The infrastructures modeled in this session can facilitate sustainable digital preservation by centralizing technical services while maintaining distributed stewardship, but are not without their challenges. âThis panel will discuss the challenges and advantages of establishing centralized digital preservation intake services at large and distributed institutions. Each panelist will present from a different stage of implementation, offering distinct perspectives on the process, followed by a moderated discussion and welcomed questions from the audience. Jess Whyte, from University of Toronto, will discuss the process of conducting a needs assessment and planning for a pilot project. Alice Prael will present the findings from a pilot centralized service for special collections at Yale University and explore the shift from a pilot project to a sustainable program. Dinah Handel will discuss the service management approach as it relates to digitization and digital preservation, as well has how Stanford has centralized their Digitization Services within the Digital Library Systems and Services department to meet stakeholder and inter-department needs. The goal of the session is to develop the discourse around infrastructure, the realities of implementation, and how the paths to maturity are not always linear or singular. The infrastructures modeled in this session can facilitate sustainable digital preservation by centralizing technical services while maintaining distributed stewardship, but are not without their challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.865 | 0.587 |
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; both teacher heads agree on what is shown here.
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".