Audio Feedback Project: A Project to Increase Social Presence in a Virtual Library and Knowledge Service
Bibliographic record
Abstract
Objective – This research project sought to determine if audio feedback in literature searches can increase the social presence of the library and create a positive view of the library service. It also explored the process of recording and sending audio feedback; tested its practicality, sustainability, and accessibility; and ascertained whether audio feedback enhanced the library’s communication, thereby creating a positive attitude toward the library and its services. Methods – The research was conducted in a small virtual library and information service. The research sample consisted of all library users and clinicians who requested a mediated literature search between July 2019 and July 2020. All participants were sent an audio commentary on their search results, recorded by the librarian, and were asked to respond to an online questionnaire. The questionnaire consisted of five statements. The study participants indicated their agreement or disagreement with each statement on a five-point Likert scale. Results – The researcher sent out 96 audio commentaries, generating 31 responses to the questionnaire. The results indicated that users felt the audio feedback improved their understanding of the results of their inquiry, made them feel more comfortable about using the library, enhanced their experience of communicating with the library and provided a better experience than just receiving an email. The responses broadly supported the contention that audio commentaries created social presence and generated a positive view of the library. Conclusion – The researcher found that delivering audio feedback was both practical and sustainable. Some consideration was given to individual learning styles and how these made audio or text feedback more or less effective. Specifically, audio feedback enhanced communications better than an email alone.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".