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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".