Bibliographic record
Abstract
What characterizes Nordic book history? This was the first thing on our minds when we considered the present special issue of Mémoires du livre – Studies in Book Culture. Setting out on the project and distributing the call for papers we hoped to come up with some sort of answer to our question. However, there is no simple single‑word or single‑sentence answer that encapsulates Nordic book history. On the contrary, several new questions arose: Are there any research topics that are especially common among Nordic book historians? And is there a special Nordic book historical approach when it comes to theory and practice? Faced with such an array of possible avenues for research, we asked ourselves: what better way to address them than by providing examples of ongoing book historical research in the Nordic countries, as well as examples of research being conducted elsewhere, but dealing with Nordic subjects? In this wide‑ranging special issue, 16 scholars explore book history from a Nordic perspective, each of them offering a glimpse of their own current research. Taken together, the articles constitute a mosaic of northern book history. They draw from range of different materials, employ several different theories and methods, and explore topics spanning from manuscript culture to audiobooks. Chronologically the articles also cover a wide period, from the early modern era up until today.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".