Lists of Opportunities: My Experience as a School Librarian During the COVID-19 Pandemic
Bibliographic record
Abstract
In this paper I share my experiences, opinions and perspectives of running a school library during the COVID-19 pandemic. I discuss the difficulties and problems I have encountered, but also the opportunities for creativity that have presented themselves. From experiencing government cutbacks to layoffs and school closures, I discuss my feelings and frustrations about COVID-19 and how it prevented me from doing my job. I demonstrate how the pandemic heightened the feeling of isolation and loneliness in a job that can already make one feel disconnected; I highlight the importance and need for human connection. I also examine the new creative opportunities that working during a pandemic has given me, like asynchronous programming, collection development, professional development and a chance to experiment or renovate. This paper is meant to highlight the importance of school libraries and start a discussion of our role before and after the pandemic. Advocacy helps ensure that school libraries remain open. My goal is to give a glimpse of day-to-day library practice in a school library during the pandemic and share ideas and information with the library and information community. My views and opinions are my own, and the context will be different in every school.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.047 | 0.018 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".