What’s Left When It’s Over: Libraries and Digital Humanities Project Preservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This is the second paper of two from the Endings Project at the University of Victoria.The first paper, by Stewart Arneil, outlines the challenges of building a sustainable digital humanities (DH) project from the perspective of a programmer who has worked with researchers to build a number of DH project sites.This second paper, written by a librarian, considers the role of libraries as DH preservation partners.A recent special issue of Digital Humanities Quarterly, edited by the Endings Project team, provides an in-depth discussion of the many sustainability challenges faced by DH researchers (Holmes et al.).In 2018, the Endings Project undertook a survey of 127 DH projects with the goal of better understanding the various ways in which DH projects come to an end.The earliest projects represented in the survey began in the 1980s, but the vast majority were started after 2001.Of those projects, spanning a four-decade period, only 24% were considered by their principal investigators to be "complete," and only about 10% were archived in a stable, long-term environment with active preservation services ("Survey Results").As digital projects proliferate in the humanities, the question of preserving non-traditional research outputs like websites, databases, and software tools becomes a pressing one.Many researchers turn to academic libraries for solutions.A recent discussion on the Humanist listserv underscores the gap between faculty expectations and library capacity (Wall et al.).In most cases, faculty are hopeful that their libraries will adopt a project wholesale and agree to keep the entire software stack-all of the different applications and dependencies that allow the application to function-viable over the long term.This is not a scalable proposition for even very well-funded libraries.The gap between what is desired by faculty and what is sustainable for libraries creates a tension that is difficult to resolve in a way that is satisfactory to both parties.When we talk about the preservation of digital objects and platforms, we must first acknowledge that "persistence is a function of organizations, not a function of technology" (DOI Foundation).This may be a slight overstatement, because of course organizations do use technology in order to preserve digital content, but the point is that technology is just that-a set of tools that are developed and used by human beings who are funded by organizations to carry out specific functions.There is no technical design choice that will absolutely future-proof information containers, particularly over the very long term.GLAM organizations (galleries, libraries, archives, and museums) are unique in their mission to collect, organize, and store information in ways that can preserve access to knowledge over hundreds or thousands of years.The deluge of digital information raises many new questions about what should be preserved, and about how libraries can organize their limited resources to take on this work.In this paper, we will examine six different approaches to digital preservation to determine the strengths and weaknesses of each, considering both technical and resource implications.The approaches are dark archiving, preserving objects and metadata in a repository, web harvesting, emulation, preservation of dynamic/social sites, and archiving static versions.
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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 | 0.000 | 0.000 |
| 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 | 0.004 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it