Charting a New Aesthetics for History: 3D, Scenarios, and the Future of the Historian’s Craft
Bibliographic record
Abstract
Innovations in computing are presenting historians with access to new forms of expression with the potential to enhance scholars’ capacities and to support novel methods for analysis, expression, and teaching. Computer-generated form can change the way we generate, appropriate, and disseminate content. If these benefits are to be realized, however, the discipline must make room for a new domain of practice-based research. The practices we have for knowledge generation were devised in association with print technology, and historians must now acquire and develop practices that can inform our use of digital forms of representation, as well as the platforms that sustain them. Les innovations informatiques donnent aux historiens l’acces a de nouvelles formes d’expression et offrent la possibilite d’accroitre les capacites des universitaires et de favoriser l’emergence de nouvelles methodes d’analyse, d’expression et d’enseignement. L’ordinateur peut changer notre facon de generer, de nous approprier et de diffuser le contenu. Pour recolter de tels fruits, la discipline doit toutefois faire place a un nouveau domaine de la recherche fondee sur la pratique. Nos pratiques de generation du savoir sont fonction de la technologie de l’imprime, et les historiens doivent maintenant acquerir et developper des pratiques qui pourront nous aider a maitriser les formes numeriques de la representation de meme que les plateformes qui leur servent d’assise.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".