Bibliographic record
Abstract
Project Endings is a collaborative SSHRC-funded project conducted by a team of faculty members, librarians, and programmers at the University of Victoria in BC, Canada, that explores questions about the ending and archiving of digital humanities (DH) projects. The main goals of Project Endings are to align the aims of faculty researchers and archivists in the long-term curation and preservation of DH projects, and to develop practical tools to assist with the archiving of both data and interactive elements of digital projects. To achieve these goals, we conducted a survey followed by a series of interviews with DH scholars across Canada and internationally about their experiences ending and archiving digital projects. In April 2021, we also hosted the Endings Symposium, where we brought together members of the Project Endings research team as well as a number of interview participants to further discuss some of the issues facing DH work. This paper will summarize the methodological foundations of the Project Endings interviews and illustrate how these foundations have been reflected in the interviews and subsequent analysis conducted by the Project Endings team. The interview process was guided by constructivist grounded theory, narrative inquiry, and phenomenology. These principles have allowed us to collaboratively co-construct knowledge with each other and with research participants. This paper will discuss the ways in which knowledge has been co-constructed over the course of the Project Endings interviews and analysis, as well as through the 2021 Endings Symposium.
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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 | 0.189 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".