Historiography’s Two Voices: Data Infrastructure and History at Scale in the Oxford Dictionary of National Biography (ODNB)
Bibliographic record
Abstract
On its release in 2004, the Oxford Dictionary of National Biography was called “the greatest book ever” and “a more enthralling read than all the novels ever entered for the Booker Prize put together.” The tabloid The Daily Mail, where these giddy pronouncements appeared, is not known for understatement, but more cautious academic researchers have long held the ODNB in similarly high esteem. Stefan Collini, writing in the London Review of Books, found himself “experiencing a rare, and wholly unironic, feeling that mixes pride and humility with a dash of wonder” when he considered “generations to come making use of this vast consolidation of scholarly accuracy for purposes of their own which may be barely imaginable to us now.” Taking into account both the hardbound version and what most assume is its digital doppelgänger at oxforddnb.com, Noel Malcolm in The Sunday Telegraph called the ODNB “an astonishing piece of work: a colossal, beautiful, fully functional and utterly user-friendly engine of enlightenment.” Reviewers’ initial responses—awe and astonishment—have, until recently, arguably been the responses most appropriate to the ODNB considered in its entirety. The enormous scope of ODNB, which is the work of roughly 10,000 scholars, runs to 60 volumes in print, and is made up of more than 62 million words, quickly defeats the capacities of even those most eager to praise it.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".