Assessment on a Dime: Low Cost User Data Collection for Assessment
Bibliographic record
Abstract
Abstract Objective – This article describes the construction and use of a low cost tool for capturing user demographics in a physical library. Methods – At the Health Sciences Library of Columbia University Irving Medical Center, we created the Tap In/Tap Out tool to learn about the demographic details of our library visitors, such as their status, school affiliation, and department. The Tap In/Tap Out tool was implemented twice for two weeks in 2013 and 2017, with users voluntarily tapping their campus ID when entering and leaving the library. We checked campus ID numbers against university databases to fill in demographic details of the library users. Results – We constructed the Tap In/Tap Out tool using a Raspberry Pi and RFID card readers mounted on a foam board poster and placed near the library entrance. Participation in the Tap In/Tap Out tool ranged from 5-7% of the library gate count numbers during the survey periods. Though low, this participation provided a useful indication of user demographics that helped to strengthen library discussions with university administration. The 2013 survey results, which showed that the library space was actively used by students from all the constituent Medical Center schools, were used to support funding justifications. The 2017 survey results, which showed continued library usage, were used to illustrate the value of the library to the Medical Center community. Conclusion – The Tap In/Tap Out tool was inexpensive to implement and provided more information about library visitors than gate counts alone. Findings from the Tap In/Tap Out results were used to demonstrate library usage and justify funding. We describe how other libraries might create and implement the tool to capture greater levels of detail about the users visiting their spaces.
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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".