The Digital Monograph and Primary Source Databases: Agenda Toward a Unified Conversation
Bibliographic record
Abstract
In the realm of scholarly research and publishing in the humanities, much interest and activity has focused on the impact of digital technology on the academic monograph, and on the application of this technology to archival collections. In terms of the former, this paper addresses the discourse of the “future of the monograph,” focusing on statements made about the digital monograph assuming new online forms. In terms of the latter, this paper comments on primary source databases. Whereas the “future of the monograph” has been approached mainly as a question of form, the matter of primary source databases has been driven by issues of content with little attention paid to the impact of the digitized format on researchers. Yet, as uniform technical objects embedded in the shared space of the Web, the digital monograph and digitized primary sources should be viewed together. Doing so will allow us to see the features—and perhaps the futures—of both more clearly, and to make better assessments of the collections supporting digital scholarship in the humanities.
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 | 0.159 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.029 | 0.090 |
| Scholarly communication | 0.081 | 0.165 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.046 | 0.057 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".