Putting Assessment into Action: Selected Projects from the First Cohort of the Assessment in Action Grant
Bibliographic record
Abstract
Jacalyn A. Kremer is a contributing author, "Honor Bound: Assessing Library Interventions into the Complex Problem of Academic Integrity." Book description: Are you new to library assessment? Are you tasked with conducting an assessment project and don't know what methods to use, or which ones are the most effective (or practical)? The methodological issues addressed in Putting Assessment into Action: Selected Projects from the First Cohort of the Assessment in Action Grant (Eric Ackermann) are based on the real world, practical experience of librarians who participated in the first cohort of the assessment in Action project. Unlike many books on this subject, this volume allows the selection of an appropriate assessment method(s) based on the activity or program being assessed without requiring extensive previous knowledge of research design, methods, or statistics. Twenty-seven cases are presented in arenas as varied as assessing fourth year undergraduate learning, first year experience, graduate student information literacy, technology facilities, assessing outreach services and space, and more. Represented are 25 U.S. institutions and two Canadian institutions and a range of types of institutions from doctoral/research universities to baccalaureate/masters granting institutions to a tribal college and a community college. This book is appropriate for professional Library and Information Science collections in all types of libraries and is particularly appropriate for immediate consideration of assessment methods.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".