Playing Well With Others: The Social Edition and Computational Collaboration
Bibliographic record
Abstract
This article draws on the Social Edition of the Devonshire Manuscript’s RDFa encoding practice as a case study of how to formalize statements about entities on the Web in a way that is machine-parsable. RDFa encoding allows machines to become collaborators with human readers in the discovery of new connections between entities (people, places, and events) even between websites. The edition’s encoding is motivated by the INKE Modelling and Prototyping team’s guiding research question about the implications and impact of real-time applications in relation to traditionally static knowledge objects. The authors argue for the value of bringing texts into communication with other texts, through RDFa, allowing virtual collaboration even when the scholars behind the projects do not know one another.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".