Marketing and Assessment in Academic Libraries: A Marriage of Convenience or True Love?
Bibliographic record
Abstract
Objective – This paper describes the process of cooperation between the Marketing and Assessment Teams at the University of Haifa in Israel, from initial apprehension about working together to the successful marketing of a suite of user studies. Methods – The first step was a formal meeting in which the leader of the assessment team explained the aims of assessment. For each assessment activity, the assessment team submitted a formal request for assistance to the marketing team, conducted team meetings on how to market each assessment, and met with the marketing team to explain the survey and receive their input on how it should be marketed. Over a 3-year period, 5 joint activities were undertaken: a 1-day, in-library use survey; a wayfinding study, in which 10 new students were filmed as they searched for 3 items in the library; 5 focus group sessions regarding upcoming library renovations; a LibQUAL+® survey measuring perceptions of service quality among the entire campus population; and an online survey of non-users of the library. The success of the assessment/marketing projects was measured by the response rates, the representativeness of the results, and the number of free-text comments with rectifiable issues. Results – Although the response rates were not very high in any of the surveys, they were very representative of the university population. With over 40% or respondents filling in free-text comments, the information received was used and applied in making service changes, including the creation and marketing of additional group study rooms, improved signage, and the launch of a “quiet” campaign. In addition, a “You said – We did” document was compiled that outlines all of the changes that were implemented since the first four surveys were conducted; this document was published on the library’s blog, Facebook page, and website. Conclusion – The number of issues that appear in the first “You said – We did” document is a testament to the close and ongoing collaboration between the two teams, from the planning stages of each survey until publication of results and notification of the changes that were implemented.
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 | 0.093 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.053 |
| Scholarly communication | 0.033 | 0.034 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.004 | 0.011 |
| 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".