Digital preservation language for agreements between libraries and publishers
Bibliographic record
Abstract
Libraries and publishers have a mutual obligation to ensure long-term preservation of the scholarly record - leveraging library expertise in preservation, publisher expertise in content production, and the author relationships and financial means of both parties. Agreements between the parties are good places to document these mutual obligations. These might be subscription agreements, open access publishing agreements, or agreements that blend the two. A review in 2022 revealed that digital preservation language in many existing agreements was vague, unclear regarding the precise content and time depth preserved, unnecessarily restrictive in terms of access and/or use, conflated post-cancellation access and long-term digital preservation and access, and was sometimes administratively burdensome to implement. It was also difficult to verify compliance with the agreements, and that the content was actually preserved properly. This paper clarifies the distinction between long-term preservation and post-cancellation access; recommends improved language to use in agreements; and offers guidance on how to negotiate the language into agreements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".