The Forest, The Trees, The Bark, The Pith: An Intensive Look at the Circulation Rates of Primary Texts in Ten Major Literature Areas at the University of Oregon Libraries
Bibliographic record
Abstract
This poster looks at the circulation rate for literary primary texts, which constitute a unique area of collecting in academic libraries: while they do not in most cases meet immediate research needs, it is assumed that libraries ought to acquire them, for reasons including future research needs, preservation of the cultural record, and the ability of members of the intellectual community to stay current, those these remain primarily tacit. The circulation trends of contemporary literary works in ten areas of literature (English, American, German, French, Italian, Spanish, Latin American, Chinese, Japanese, and Russian) over the past twenty years at the University of Oregon Knight Library are presented and the circulation turnover rate (CTR), for each of these subject areas are presented. Sample graphs allow for the comparison of circulation rates and numbers of books across time, and serve as examples of the utility of such visualizations of the numbers. The key question raised by the study is what makes a good CTR for a particular region of the collection? The poster concludes by summarizing the considerations that bear on the interpretation of the CTR as an index of how the collection is “working.”
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".