Introduction: From Technical Standards to Research Communities – Implementing New Knowledge Environments Gatherings, Sydney 2014 and Whistler 2015
Bibliographic record
Abstract
e image of the monastic humanities scholar toiling away in a paper-laden faculty office to produce a scholarly monograph is a popular stereotype.It is popular in part because it simplifies the oen abstract, organic, and even rambling processes involved in humanities-based research.As humanists increasingly collaborate by using online and digital tools, however, the messy office metaphor requires a literal and systematic overhaul.e collaborative processes between humanities scholars and students takes this stereotype and finds new ways to create and mobilize knowledge generated in digital environments.Drawing from two gatherings of the Implementing New Knowledge Environments (INKE) project, the articles collected in these three issues (6.2, 6.3, 6.4) of Scholarly and Research Communication (SRC) work to do just that.As part of an ongoing conversation in SRC (Arbuckle, Crompton, & Mauro, 2014), these issues will continue to describe new ways humanities researchers, publishers, and policy makers can collaborate effectively to make the most of the new affordances of computational tools and methods.On December 8, 2014, researchers, students, librarians, and other participants gathered together in Sydney, Australia at the State Library of New South Wales for the 7th annual INKE Birds of a Feather conference, "Research Foundations for Understanding Books and Reading in the Digital Age: Emerging Reading, Writing, and Research Practices." On January 27 and 28, 2015, a similar group of stakeholders met in Whistler, BC, Canada, at the Nita Lake Lodge for the second year in a row to discuss "Sustaining Partnerships to Transform Scholarly Production." 1 e events were hosted by INKE and sponsored by
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".