Bibliographic record
Abstract
Welcome to the second issue of IASSIST Quarterly for 2024, IQ 48(2). It was wonderful to meet so many old and new colleagues at the Best IASSIST Ever in Halifax. It was really inspiring to learn about all the great work that is being done by members of this community. For those of you who presented, please consider turning your conference presentation or poster into a paper and submitting it to IQ. This will allow you to share your expertise with a wider audience. If you were not able to attend the conference, you may have missed the announcement about the winner of the IASSIST Conference Paper Competition. This year’s winner is the paper “How are we FAIR-ing? Creating a FAIR Self-Assessment Checklist for Data Repositories” by Lauren Phegley and Lynda Kellam. In the paper the authors describe a project in which a data repository’s staff wanted to gauge how well they were enabling FAIR principles. A small team from Penn Libraries found that much of the literature about FAIR was from the perspective of data creators, so they developed a FAIR Principles Self-Assessment Tool for repository teams. We look forward to publishing this paper in a future issue. We would like to take this opportunity to encourage you to look ahead to submitting your papers for next year’s paper competition. In addition to bragging rights, the award incudes free registration for the first author to the following year’s IASSIST conference. The four papers included in this issue of IQ introduce tools developed in several institutions, representing a wide geographic diversity, to assess and resolve operational challenges. In the article titled ”Research analysis: A World Data System and Canadian CoreTrustSeal Cohort needs assessment,” Lee, Gonzalez, Payne and Goins describe how they designed a method to identify the needs and challenges faced by members of the World Data System (WDS) and Canadian CoreTrustSeal Pilot. They also describe the assessment tool they developed and the overarching challenges and goals identified through the usage of this tool. Based on their findings, they provide recommendations on how best to assist the WDS members and the cohort of Canadian data repositories. Constanzo and Cooper, in their article ”Developing institutional research data management strategies in Canada: Setting the foundation for stronger partnerships and collaborations,” describe national surveys developed by Research Intelligence Expert Group (RIEG) to gauge institutions’ readiness for developing an institutional RDM strategy required by the Government of Canada’s Tri-Agency. The first survey was conducted in 2019 and a follow-up survey in 2022 in order to assess the progress of institutions in creating their institutional strategies and identifying additional challenges. The authors and report the findings and recommendations from their study and share their survey instruments. In ”Enhancing FAIR compliance: A controlled vocabulary for mapping social sciences survey variables,” authors Bach and Klas introduce the GESIS Controlled Vocabulary (CV) for Variables in Social Sciences Research Data. This CV is designed to enhance semantic interoperability across various organizations and systems, and facilitates harmonization across different study waves. This endeavor aligns with the FAIR data principles, and aims to foster a more integrated and accessible research landscape. Obasola and Usman in their article ”Digitising old Yoruba newspapers at Kenneth Dike Library,” describe in detail the digitisation of a collection of old Yoruba newspapers stored at Kenneth Dike Library in Ibadan, Nigeria. The project was undertaken in order to preserve this historical and delicate material, which includes rich details of local history. In addition to providing a detailed workflow, the authors share lessons learned. We hope you enjoy reading and wish you a productive summer. Ofira Schwartz and Michele Hayslett, June 2024
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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.019 |
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".