The finals stretch: exams week library outreach surveyed
Bibliographic record
Abstract
Purpose – The purpose of this article is to explore the outreach programming and support services offered at academic libraries during final exams week. The article discusses the need to provide this specialized programming, and its impact on the role of the librarian, and the use of library space. Design/methodology/approach – A 21-question online survey was sent to nine library listservs. Respondents were asked about their library’s planning, implementation and assessment of their programming. Participants described their library’s final exams week outreach activities and offerings. Findings – The survey garnered 279 responses. Nearly 40 per cent of respondents collaborate with campus and non-campus partners to provide programming. Most common offerings include extended library hours; therapy animals; as well as games; and arts and crafts. Nearly 90 per cent of respondents reported utilizing virtual media outlets for communication of final exams week events. Most common challenges included lack of funding and staffing issues. Practical implications – The survey results reflect the efforts of librarians and library staff members who are addressing the study break needs of students during the most stressful time in the semester. Originality/value – This paper provides an overview of academic library final exams week outreach initiatives and activities at institutions located in the USA and Canada.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".