Transformation Through Integration: The Renaissance Knowledge Network (ReKN) and a Next Wave of Scholarly Publication
Bibliographic record
Abstract
This article reflects on the first six months of funded research by the Renaissance Knowledge Network (ReKN), focusing especially on the possibilities for interoperability and metadata aggregation of diverse digital projects, including but not limited to Early English Books Online—Text Creation Partnership; the Iter Bibliography; the Canadian Writing Research Collaboratory; the Advanced Research Consortium network; Editing Modernism in Canada; the INKE working groups; and several other, smaller projects. This article also considers how internetworked resources and a holistic scholarly environment should incorporate and build on existing publication and markup tools. Key to this process of facilitating new forms of scholarly production are including possibilities for middle-state publication; exporting both primary and critical content; and forming new types of technologically facilitated scholarly communities.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".