Does the READ Scale Work for Chat? A Review of the Literature
Bibliographic record
Abstract
Objective – This review aims to determine the suitability of the READ Scale for chat service assessment. We investigated how librarians rate chats and their interpretations of the results, and compared these findings to the original purpose of the Scale. Methods – We performed a systematic search of databases in order to retrieve sources, applied inclusion and exclusion criteria, and read the remaining articles. We synthesized common themes that emerged into a discussion of the use of the READ Scale to assess chat service. Additionally, we compiled READ Scale designations across institutions to allow side-by-side comparisons of ratings of chat interactions. Results – This review revealed that librarians used a variety of approaches in applying and understanding READ Scale ratings. Determination of staffing levels was often the primary goal. Further, librarians consistently rated chat interactions in the lower two-thirds of the scale, which has implications for service perception and recommendations. Conclusion – The findings of this review indicated that librarians frequently use READ Scale data to make staffing recommendations, both in terms of numbers of staff providing chat service and level of experience to adequately meet service demand. Evidence suggested, however, that characteristics of the scale itself may lead to a distorted understanding of chat service, skewing designations to the lower end of the scale, and undervaluing the service.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".