Timmins Public Library Internship
Bibliographic record
Abstract
For my SASAH experiential-learning project, I worked at the Timmins Public Library over the summer of 2020. While it was my third year working as a summer student at the library, this one offered new challenges and unique learning opportunities as I was working in the midst of a pandemic. My main responsibilities were working on the newly implemented curbside pick-up program and assisting patrons over the phone. But working at the library is so much more than just checking out books. Throughout the summer, I created social-media content, organized a StoryWalk event, and provided vital resources to the community. The library is an important pillar in the community as it provides free access to information and books, directories to community resources, computer and internet access, and a safe space for all community members, including patrons experiencing homelessness. As part of the library’s dynamic team, I helped adapt library resources to safely meet community needs and undertook a variety of projects that allowed me to exercise creativity and have a meaningful impact in the community. Through this work, I gained invaluable communication, team-work, and problem-solving skills and developed as a professional and a person.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.483 | 0.206 |
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".