A Call to Develop Standards for Those Delivering ‘Research Practice’ Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.016 | 0.042 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| 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 it