An Analysis of RDM Job Postings in Canadian Academic Libraries
Bibliographic record
Abstract
Note: While we initally planned to focus on job postings in Canadian academic libraries, we have expanded this to include RDM job postings in Canada. We made this decision so as not to exlcude postings from other types of insitutions in the Canadian research landscape. This talk discussed prelimanry steps in our work to analzye RDM job postings in Canada over the last decade. Specifically, we explored the following research questions: (1) what terminology is used; (2) what are the requirements listed; (3) what are the responsibilities and characteristics of the positions; (4) have there been changes over time; and (5) how do our findings compare to similar studies?This study was born from the desire to understand how institutions have been planning for the future of RDM support in Canada. The RDM landscape in Canada has changed significantly in the past decade. The development of the Tri-Agency RDM Policy, changes in journal/publisher requirements, and an increased emphasis on open science have changed the way researchers are expected to manage their research data and, consequently, the types and volume of support they need and that are provided by institutions.The results of this study will help the Canadian RDM community gain a deeper understanding of the role libraries play in supporting RDM and the skills and experience desired when hiring RDM professionals. The findings could also help guide professional development initiatives and could be compared to Canadian LIS curricula to uncover gaps in training for the next generation of information professionals.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".