Mobile Technology and Academic Libraries: Innovative Services for Research and Learning. Robin Canuel and Chad Crichton, eds., for the Association of College and Research Libraries. Chicago: American Library Association. 2017. 284p. $68.00.
Bibliographic record
Abstract
Providing content, collections, and services to mobile users is increasingly important in contemporary libraries. In Mobile Technology and Academic Libraries: Innovative Services for Research and Learning , Robin Canuel and Chad Crichton curate a collection of practical case studies of libraries adapting technologies, piloting new initiatives, and building new tools to meet the needs of mobile users. This volume is oriented toward academic libraries but is diverse within this constraint. Canuel is the Head of the Humanities and Social Sciences Library at McGill University, and Crichton is a Liaison Librarian at the University of Toronto Scarborough. Both have presented and published on the use of mobile technologies in libraries. The case studies come from academic institutions, but the selection features libraries of different types, including research libraries, archives, law libraries, and health sciences libraries. The volume also includes examples from functional units across the library, including public services, collections, web services, and instruction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.028 |
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".