Libraries: Sustaining the Digital Reader Experience
Bibliographic record
Abstract
As librarians involved with two online reading groups, Read Watch Play (a regular themed Twitter chat) and Read With Me (a live Web-based, face-to-face discussion using Adobe Connect), we consider how we present text in these specific online environments, and how this impacts the reader’s experience. Formats and interfaces used by both groups result in different types of reading experiences – including brief, mobile-based reads (Twitter chats) or more in-depth reads (blogs). Both groups recognize that reading is a critical skill required for discussions about books. We also recognize the value of face-to-face online discussions. This presents challenges for libraries in how they connect and interact with readers: encouraging reading discussions online, offering tools focused on reading, and connecting these with the full range of reading materials available in libraries, both online and off.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.040 | 0.023 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".