Piloting a Library-Led Online Academic Skills Orientation Program
Bibliographic record
Abstract
In August 2021, we piloted an online academic skills orientation program for incoming undergraduate and graduate students. The program featured a range of synchronous online sessions that provided students an opportunity to learn from librarians, learning specialists, peer mentors, professors, academic advisors and other campus partners during presentations, panels and Q&As. The program was attended by 548 unique students with overall workshop attendance totaling 1310 over a four-day period. Due to the success of the 2021 pilot, we ran another iteration of the program in August 2022, which included both online and in-person elements. The three-day online program was attended by 309 unique students with overall workshop attendance totaling 1084 while the one day in-person program was attended by 37 students. Approximately 8% and 20% of program attendees completed program surveys in 2021 and 2022 respectively. While the low response rates make it difficult to generalize about the data, feedback overall was positive, with most respondents indicating that they found the experience to be valuable. We conclude by encouraging other libraries to consider the role that they play in familiarizing students with university supports and services and to take a leading role if such programming is not currently in place at their institution.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".