Bibliographic record
Abstract
In March and April of 2021, my co-investigators and I conducted semi-structured interviews with academic librarians across Canada about their work during the COVID-19 pandemic, which included their thoughts about going “back to normal.” Most participants were resistant to returning to the “old normal” without myriad changes inspired by the COVID-necessitated adaptations. However, there were concerns raised about whether or not their ideas would be implemented or even heard by their administrations. Additionally, many participants felt caught between proving their value through productive (and measurable) labour and the care-work that felt necessary and pressing but was not externally validated. This paper highlights the need for refocusing on building library collegial governance structures that include all library workers. As well, there is indication that the COVID-19 pandemic presents a unique opportunity to do so, as, removed from the “sacred space” (Ettarh 2018) of the library building, participants showed resistance to the austerity narratives typically invoked during a crisis. Embodying our values starts with establishing and building on shared library governance structures. If the changes inspired by COVID are to come to pass, then our vision of care and relationship-building must be inclusive to our own workers, to harness our collective power to build a future that works for everyone.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.024 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".