A Call to Develop Standards for Those Delivering ‘Research Practice’ Training
Bibliographic record
Abstract
The nature of the research endeavour is changing rapidly and requires a wide set of skills beyond the research focus. The delivery of aspects of researcher training ‘beyond the bench’ is met by different sections of an institution, including the research office, the media office and the library. In Australia researcher training in open access, research data management and other aspects of open science is primarily offered by librarians. But what training do librarians receive in scholarly communication within their librarianship degrees? For a degree to be offered in librarianship and information science, it must be accredited by the Australian Library and Information Association (ALIA), with a curriculum that is based on ALIA’s lists of skills and attributes. However, these lists do not contain any reference to key open research terms and are almost mutually exclusive with core competencies in scholarly communication as identified by the North American Serials Interest Group and an international Joint Task Force. Over the past decade teaching by academics in universities has been professionalised with courses and qualifications. Those responsible for researcher training within universities and the material that is being offered should also meet an agreed accreditation. This paper is arguing that there is a clear need to develop parallel standards around ‘research practice’ training for PhD students and Early Career Researchers, and those delivering this training should be able to demonstrate their skills against these standards. Models to begin developing accreditation standards are starting to emerge, with the recent launch of the Centre for Academic Research Quality and Improvement in the UK. There are multiple organisations, both grassroots and long-established that would be able to contribute to this project.
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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 | 0.462 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.047 | 0.061 |
| Open science | 0.018 | 0.034 |
| Research integrity | 0.038 | 0.079 |
| Insufficient payload (model declined to judge) | 0.010 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier 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".