Collections Data, Tools, and Strategy: Applying R, Tableau, and Excel to Print 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
As is the case at most academic libraries, collection assessment has become an essential component of collection management and development work. Although much of the assessment focus has disproportionately fallen on e-resources, print collections remain fruitful areas for evaluation and review. At Emory, print collections, including a complex approval plan, continue to be a significant component of our overarching collection strategy (in volume and expenditure). However, shifting priorities for library space and the growth of interdisciplinary programs and centers within the University are placing a higher demand on subject librarians for communication and coordinated decision-making regarding print acquisitions. As a result, we are currently preparing for a comprehensive print collection review, of which the approval plan is an integral component. This assessment will inform a more coherent print strategy, which effectively and efficiently meets research and teaching requirements as well as administrative needs. Using data cleaning and visualization tools, such as R, Excel, and Tableau, we have enriched our local usage data with detailed Gobi approval data (e.g., series, publisher, subject, etc.) and profile parameters. Merging these data types and enriching local use data will allow us to analyze the print collection in a more nuanced fashion and ask questions that do not require the LC classification framework. This analysis considers the development of additional tools and approaches that facilitate subject specialist communication with collection management and overall collaborative decision-making, especially in cross disciplinary areas.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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