Evaluating the Impact of a Proactive Chat Feature on Reference Questions, Their Complexity and Subject Matter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to investigate the impact of a proactive chat widget on the types of questions received and their complexity at the McGill University Libraries. A qualitative analysis of the proactive chat transcripts using coding for types and subject of questions as well as READ scale level was used in addition to a quantitative analysis of the pages where users accessed the proactive chat feature. The author determined that including a proactive chat feature on webpages related to research topics significantly increased the number of reference interactions as well as the complexity level of the questions asked by patrons. She also identified that questions related to law were the most common on proactive chat and that the pop-up on the law research guide was the most used which indicates that users received point-of-need help related to this topic. This is the first study to investigate the link between placing a proactive chat widget on research related pages and the increase in reference questions. Similarly, it is one of the few studies identifying the main referral pages for proactive chat and how it impacts the subjects of the questions received.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it