Bibliographic record
Abstract
Objective – Measures of trends in Iowa State University library website visits per student/faculty/staff headcount show decreased use. Analysis was conducted to test for a relationship between this decrease and decreasing graduate/undergraduate enrollment ratios and decreasing visits to a popular digital collection. The purpose was to measure the influence of these factors and to produce an adjusted measure of trend which accounts for these factors. Methods – Website transaction log data and enrollment data were modelled with Box and Jenkins time series analysis methods (regression with ARMA errors). Results – A declining graduate to undergraduate enrollment ratio at Iowa State University explained 23% of the innovation variance of library website visits per headcount over the study period, while visits to a popular digital collection also declined, explaining 34% of the innovation variance. Rolling windows analysis showed that the effect of the graduate/undergraduate ratio increased over the study period, while the effect of digital collection visits decreased. In addition, estimates of website usage by graduate students and undergraduates, after accounting for other factors, matched estimates from a survey. Conclusion – A rolling windows metric of mean change adjusted for changes in demographics and other factors allows for a fairer comparison of year-to-year website usage, while also measuring the change in influence of these factors. Adjusting for these influences provides a baseline for studying the effect of interventions, such as website design changes. Box-Jenkins methods of analysis for time series data can provide a more accurate measure than ordinary regression, demonstrated by estimating undergraduate and graduate website usage to corroborate survey data. While overall website usage is decreasing, it is not clear it is decreasing for all groups. Inferences were made about demographic groups with data that is not tied to individuals, thus alleviating privacy concerns.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".