Chapter 10</br>The textual habitat: The development of new knowledge environments
Bibliographic record
Abstract
In contemplating the design and implementation of new knowledge environments, what can we learn from book history and from the natural world about how environmental systems form, develop, and thrive? This essay uses the theory of ecodynamics to theorize the development of new knowledge environments for academic study, with illustrative examples from the history of the Christian Bible, and concludes by deriving some principles for those of us working to develop new digital scholarly resources. En contemplant la conception et l’élaboration de nouveaux environnements de connaissances, que pouvons-nous apprendre de l’histoire du livre et du monde naturel sur la façon dont les systèmes environnementaux se forment, se développent et prospèrent? Cet article fait appel à la théorie de l’éco-dynamique afin d’élaborer une théorie d’élaboration de nouveaux environnements de connaissances pour les études universitaires, avec des exemples illustratifs de l’histoire de la bible chrétienne, et conclut en obtenant quelques principes pour ceux d’entre nous qui tentent d’élaborer de nouvelles ressources numériques érudites.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".