Lessons from Ithaka S+R on Research Practices in the Disciplines: What Have We Learned? What Should We Do?
Bibliographic record
Abstract
It is a byword of the study of academic research that disciplines mean differences. The series of studies underway at Ithaka S+R (with library partners) shows how scholars and scientists understand “Changing Research Practices.” The project’s goal is to guide libraries toward the most fruitful forms of support for research, enhancing the scholarly workflow according to disciplinary routines and innovations. Launched in 2012, nine reports have been published thus far, with others planned or anticipated. The disciplines range from history to public health, from chemistry to Asian Studies. The interview-based studies show how scholars manage their methods, and the opportunities and obstacles they face as the availability of resources in several media expand and research technologies evolve. The Ithaka S+R studies represent a unique collective portrait of scholars at work, loyal to research conventions but encountering new tools for inquiry. The reports help us understand how disciplinary habits shape expectations and experience, and what might be done to serve scholars working at change in research practices, particularly the introduction of new technologies. The reports are seen against the backdrop of views among library leaders and librarians themselves about the evolution of the liaison role, including how it can be fitted to the needs of scholars in an evolving research environment.
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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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".