Bibliographic record
Abstract
Welcome to this special double issue of IASSIST Quarterly for the year 2023, IQ vol. 47(3-4). We are delighted to close out the year by offering the second special issue of the IASSIST Quarterly to showcase articles from the Africa workshop. Articles in this issue were presented in the second Africa Workshop which was held in Ibadan, Nigeria, in October 2022. Guest editors Winny Nekesa and Robert Burosta have again expertly steered the editorial process to bring us this research. While their Guest Editors’ Notes describe the included articles, we would like to use this space to share a number of announcements about administrative work on the journal. First, please join us in welcoming four new Editorial Board members for a four-year term: Robert Stalone Buwule, Mbarara University of Science and Technology, Uganda Winny Nekesa, National Social Security Fund, Uganda Deborah Wiltshire, GESIS, Germany, and Ryan Womack, Rutgers University, United States With these appointments, we achieve two goals. First, we stagger the terms of service of Board members so that only half will roll off the Board at any one time, ensuring continuity of knowledge moving forward. Second, we better diversify geographic representation on the Board to reflect IASSIST’s international membership. Winny and Robert bring experience as IQ guest editors to the Board. Deborah and Ryan bring perspective from the IASSIST Administrative Committee. Over the coming year, IQ editorial staff and Board members will be exploring a variety of changes to the journal, many of which were proposed by you, the membership. We’ll keep you informed as we make decisions on various of those suggestions. Several advances that we have already accomplished are to behind-the-scenes processes but may directly benefit authors who publish with us as well you, our readers. Working retrospectively to the last issue, 47(2), as well as for all issues going forward, the editorial staff have published the reference lists of all articles as metadata. This complies with I4OC, a standard that asks for citations to be structured, separable, shareable, and freely accessible (to both human and automated harvesting), resulting in citations that are index-able and searchable. Citation-tracking services like Crossref also require this. The end result is that people searching for any of the sources listed in our articles will find our articles, which over time may result in greater research impact for our authors. Reference linking will also expose articles to new tools, such as OpenAlex, an open citation metrics tool that can help measure impact. We thank our managing editor, Phillip Ndhlovu, for his effort in effecting this change. The second change was made by the Library Open Publishing and Open Education staff at the University of Alberta, whose work supports the Open Journal System platform on which the journal is hosted. Their efforts not only keep journal production flowing smoothly, they work continually to improve the technical systems to uphold and improve open access to our content. In this case, they have implemented a Research Organization Registry (ROR) feature to allow authors to select their organizational affiliations from the list of organizations in ROR. This will not only speed the information input authors must complete during submission, but also standardize it to be represented consistently within the journal, and make it clear and accurate for sharing in external systems such as DOAJ and CrossRef. Finally, we want to mention a new content feature premiering in this issue. Following the receipt of a Letter to the Editor (to our knowledge the first ever), we’ve added a new section to the journal’s infrastructure to accommodate such conversations. We hope you will enjoy reading this commentary which extends the implications of the Hertzog, et al. article in 47(2) to a different type of personally identifiable data, DNA. We invite you to take advantage of the option to use this feature in future to correspond with our articles. We wish you all the best for whichever end-of-year holidays you celebrate! We look forward to showcasing your work through the IQ in the coming new year, both in the research you submit for publication and in implementing your ideas for evolving the journal’s content and production. Ofira Schwartz and Michele Hayslett, December 2023
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.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.143 | 0.081 |
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".