The open technology specialist at the University of Toronto Libraries: A comprehensive approach to Wikimedia projects in the academic library
Bibliographic record
Abstract
Wikipedian-in-Residence (WIR) programs are becoming more common in academic libraries. Although they hold a great deal of promise, they are often limited in scope given their frequently short-term and sometimes part-time nature. After a successful one-year, part-time WIR pilot, the University of Toronto Libraries (UTL) has piloted a one-year, full-time Open Technology Specialist (OTS) role to build upon the WIR’s accomplishments and allow for a more comprehensive approach to Wikimedia activities in the library. Through extensive research, outreach, and relationship-building, the OTS has considerably expanded the scope of WIR’s activities to advance a wide range of institutional strategic priorities for the long term. In line with UTL’s commitment to barrier-free access to all of the right information, the OTS incorporates Wikimedia activities into existing workflows across the library system in ways that prioritize support for historically excluded communities and collections while being sensitive to issues of access and description. In its pilot year, the OTS has created a network out of previously isolated Wikimedia engagement across the library system, trained staff, and volunteers across and beyond UTL, and helped launch formal projects that deepen institutional engagement. The OTS has also continued to contribute to Wikipedia, expanding their editing scope to the appropriate use of archival sources and the development of tools, which help bridge the gap between Wikipedia and Wikidata. Through the OTS, UTL has systematically deepened its contributions to the open Web. The UTL OTS pilot experience has demonstrated that positions dedicated to engagement in Wikimedia or other open technologies hold a great deal of potential and are worthy of further consideration for ongoing investment of staff and budget resources by academic libraries.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".