Bibliographic record
Abstract
This deposit gives access to the three NEXUS files generated by the Shaw/Robinson AHRC Commedia project, as described in the “Phylogenetic Analysis” section of the editorial matter of that publication: The file M2R1L0.nex including Martini’s collations (labelled “Mart-c2” or “M2” in our terminology) in preference to those of the original Aldine edition; the corrections of the “c1” scribe of Rb (“Rb-c1” or “R1”: in fact, the original scribe correcting his own work) in preference to the original readings in that manuscript; and the original readings of LauSC (“LauSC-orig” or “L0”) and of all other witnesses; The file M2R1L2.nex also includes the Martini collations (“M2”) and the corrections by the original hand in Rb (“R1”), but instead of the original readings includes the corrections by the second hand in LauSC (“LauSC-c2” or “L2”); The file M0R1L0.nex includes the original text of the Aldine edition, the corrections by the original hand in Rb (“R1”), and the original readings in LauSC. Prepared and published in Prue Shaw's edition of Dante's Commedia, Scholarly Digital Editions (Saskatoon) and SISMEL (Florence), November 2010.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.414 | 0.246 |
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".