Bibliographic record
Abstract
The Precarity in Libraries research project was started in 2017. Our goal is to provide information on the extent and effects of precarious work in Canadian libraries. This OSF page collects the presentation slides, data, and other content created over the span of the project. The current researchers are Adena Brons and Chloe Riley. They are conducting a longitudinal study starting in 2026. The original research team also included Ean Henninger and Crystal Yin (2017-2022). Our primary published work consists of: -- Semistructured interviews with library workers on issues associated with precarious library work (https://doi.org/10.21083/partnership.v14i2.5169) -- Coding and analysis of postings on the Partnership Job Board with an eye towards precarity (https://doi.org/10.18438/eblip29783) -- A book chapter on precarious employment as a dysfunctional practice in libraries (available at 10.4324/9781003159155-4 , preprint at https://summit.sfu.ca/item/21916) The project is based on the unceded and traditional territories of the xʷməθkwəy̓əm (Musqueam), Sḵwx̱wú7mesh Úxwumixw (Squamish), Səl̓ílwətaɬ (Tsleil-Waututh), kʷikʷəƛ̓əm (Kwikwetlem), and Katzie First Nations (Burnaby, Vancouver, and Surrey, British Columbia).
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.040 | 0.031 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".