How Do You Measure Up? A Study of Experience, Confidence, and Expertise by Canadian Academic Librarians Supporting Chemistry Instruction and Research
Bibliographic record
Abstract
Academic librarians, as liaison or subject specialists, bring varying disciplinary knowledge and experience to their instruction and research roles. This inquiry invited librarians at public Canadian universities to participate in a survey on how confident they are in providing chemistry instruction and research support. Forty-eight of sixty-four librarians (75 percent) completed the instrument between November 2023 and January 2024. Follow-up focus groups asked the eight participants to comment on the survey results and respond to semi-structured questions. Survey results and focus group transcripts were analyzed for emergent themes: (1) the importance of background, expertise, and experiences; (2) librarians’ confidence levels; (3) the challenge of being stretched too thin with additional responsibilities and tasks; (4) the pervasive influence of artificial intelligence; and (5) strategies to remain current with disciplinary pedagogy, scholarship, and research. This study sheds light on the importance of liaison librarians’ disciplinary and subject knowledge, expertise, and experience when partnering with faculty, staff, and students.
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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".