Open Infrastructure and the Threat of “Vanishing” Journals: Leveraging Open Knowledge Commons, Open Source Software, and DIY Solutions to Preserve Humanities and Social Sciences Research
Bibliographic record
Abstract
Academic journals, institutional repositories, and emerging digital technologies have played a crucial role in providing access to scholarship. However, free and unfettered access to research is not a given—nor are the digital infrastructures through which open research is published and made accessible immune to commercial enclosure or obsolescence. The threat of “vanishing” digital publications also remains a very real threat, and open-access and humanities and social sciences (HSS) journals are particularly at risk of disappearing. In this paper, we aim to address the related issues of access to, and preservation of, HSS research by examining our own experiments with open methods and tools for the (re)publication of open-access scholarship via open infrastructure. As part of this process of self-examination, we focus on one infrastructural initiative that is equipped to support this work: the Canadian-based HSS Commons. In the process, we also invite consideration of how low-budget, DIY-style innovation and experimentation in the realm of digital research software constitute valid, crucial forms of humanistic intervention and activity. To do so, we discuss a project that emerged from the HSS Commons’ collaborative partnership with Iter Canada: a large-scale migration of open-access back issues from scholarly journals or book series operated by Iter. In conclusion, we reflect on the larger significance, potential wider application, and limitations of such interventions. Indeed, while there are many possible benefits to the workflow we developed—which resulted in the publication of over 6,000 publications in the HSS Commons repository, and which we hope will serve as a model for other groups or journals interested in backing up and increasing the discoverability of their own research—our work on this project also highlighted the many methodological, infrastructural, and institutional challenges that still face those who may be interested in pursuing open scholarship of this kind.
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 | 0.072 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.061 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".