Developing Digital Scholarship: Emerging Practices in Academic Libraries. Alison Mackenzie and Lindsey Martin, eds. Chicago: ALA Neal Schuman, 2016. 184p. Paper, $70.00 (ISBN: 978-0-8389-1555-4). LC 2017289052.
Bibliographic record
Abstract
This work is a welcome addition to published research in the area of digital scholarship, boasting an international lens and the helpful integration of the theoretical with the practical. The editors, Alison Mackenzie and Lindsay Martin, both from Edge Hill University in England, bring to the work their ample leadership experience in the areas of e-learning and learning technology. This book will be of greatest value to those in the academic library community with a focus in the area of digital scholarship. Those charged with leadership in this space will find inspiration from the authors, including strategies for repositioning the library as an expert partner as well as innovative suggestions for strategic expansion in a time of scant resources. All readers will welcome the generous integration of case studies illustrated with helpful visuals. Readers seeking specific counsel in the area of digital humanities will find only thematic parallels.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".