Augmented Criticism, Extensible Archives, and the Progress of Renaissance Studies
Bibliographic record
Abstract
In the three decades since the rise of New Historicism, Renaissance studies has progressed through extensions of scholars’ archival reach to new objects for new interpretations. The future will bring expansions on a larger scale, like those we now witness in English print archives. Machine-readable transcriptions of some fifty thousand texts now enable scholars to use algorithms that tell us things about them that are true, yet can only be known in the future. This is an argument not for an algorithmic criticism but for an augmented criticism, in which human judgments are the origin and outcome of algorithmic research methods. It sketches the emergent methods that are possible only in 2015, yet will do for the archival humanities what telescopes did for astronomy. Durant les trois décennies qui ont suivi l’émergence de la nouvelle histoire, les études de la Renaissance ont développé grâce à un travail approfondi d’archives de nouvelles données à interpréter. Des développements similaires de plus grande ampleur nous attendent, tels que ceux que nous observons dans l’étude des archives imprimées anglaises. Des transcriptions pouvant être analysées par des logiciels permettent maintenant aux chercheurs d’utiliser des algorithmes révélant de nouveaux faits réels, et pourtant inaccessibles avant aujourd’hui. Il s’agit d’un argument non pas en faveur de la critique algorithmique, mais en faveur d’une critique plus vigilante, assurant que le jugement humain est bien au centre des hypothèses et des résultats des méthodes de recherche algorithmique. Cet article fait un portrait des méthodes émergentes qui ne sont possibles qu’en 2015, et qui pourraient avoir le même effet que le télescope pour l’astronomie.
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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 | 0.060 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.030 | 0.043 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".