Book Review: Using Digital Analytics for Smart Assessment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
using-digitalanalytics-smart-assessment When library users interact with digital interfaces for services and resources, they generate a lot of data.How to access and assess this data is the focus of Using Digital Analytics for Smart Assessment by Tabatha Farney.This book aims to provide an introduction to the topic of digital analytics, an outline of the tools available, and a discussion of the challenges and successes in applying analytical assessment to library services.Farney is the Director of Web Services and Emerging Technologies for the Kraemer Family Library at the University of Colorado, Colorado Springs, and she is the co-author of Web Analytics Strategies for Information Professionals: A LITA Guide, published in 2013.In Using Digital Analytics for Smart Assessment, Farney defines digital analytics as "the digital data describing the use and users of online content.For libraries, digital data includes, but is not limited to, use from various library websites, electronic resources, online collections, and even social media" (pg.ix).The book is broken down into two sections: the first, authored by Farney, provides an understanding of the various digital analytics available and how to implement and use them.In the second section, contributed chapters provide an overview by multiple librarians on the various projects they have initiated using digital analytics.These topics include library websites, collections, and social media; which all together provide a wide range of practical ways to implement the use of analytics in library services.Farney states that "regardless of your job title, accessing and analyzing digital data is essential for assessing library services in the online and offline world.This book is written for anyone interested in analytics" (pg.x).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it