Hiring Library Technicians in Academic Libraries
Bibliographic record
Abstract
Using métissage as a method of inquiry, this paper is the outcome of reflections of a Canadian academic library hiring committee that consists of both librarians and library technicians that disrupted local hiring practices in the effort to create a more human-centred, inclusive, and thoughtful process when hiring for two library technician vacancies. Through the writing and mixing of texts, three themes emerged that capture the shared experiences of the committee and serve as an example of how reflective practice can take shape among different types of employees in a busy academic library. This process helped to empower members of the hiring committee to question and contribute to the hiring process in new ways. Despite the limits of time, this project reveals that efforts can be made to create a space for a hiring committee, comprised of library employees with different levels of workplace power, to critique and modify practices to improve approaches to hiring. These improvements go beyond creating a welcoming environment for candidates and include changes to the way current employees feel about their contributions and their engagement in the work.
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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.047 | 0.015 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".