Notes from the field: Three Wikimedian-in-Residence case studies
Bibliographic record
Abstract
When the University of Alberta Library hired its first Wikimedian-in-Residence (WIR) in 2019, the team had difficulty finding detailed information about how to plan for a WIR and set up the role for success. This chapter details two Wikipedia residencies that served as a guide for the Alberta team in building their WIR project. Case studies of the University of Toronto and Concordia University in Montréal are presented alongside a case study of the University of Alberta. Each study includes details about how the role was approved and funded, how hiring decisions were made, how the WIR focused their efforts, and the impact at their institution. Together, these three examples demonstrate the variety of options for funding and hiring a WIR role and for the focus of the WIR’s work in their term. The chapter poses concrete questions for librarians considering implementing a WIR role at their institution and offers recommendations from each WIR experience as guidance in answering those questions.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".