Collection Usage Pre- and Post-Summon Implementation at the University of Manitoba
Bibliographic record
Abstract
Objectives – This study examines the use of print and electronic collections bothbefore and after implementation of Summon at the University of Manitoba Libraries.Summon is a web-scale discovery service which allows discovery of all of thematerials the library owns or has access to from a simple search box on the library’sweb page. Methods – COUNTER statistics were used to determine database, e-journal, and ebookstatistics, including database search statistics (DR1) from the COUNTERDatabase Report 1, full-text article downloads from the COUNTER Journal Report 1(JR1), and successful section search requests from the COUNTER Book Report 2 (BR2)for electronic resources. Sirsi, the University of Manitoba’s integrated library system,provided statistics on checkouts for the libraries’ circulating print monograph andserial collections. The percentage change from the pre-Summon implementationperiod to the post-Summon implementation period was calculated and these numberswere used to determine whether usage had increased or decreased for both print andelectronic collections. Results – As expected, searches in citation databases decreased because searches wereno longer being carried out in the native database as the metadata from the databaseis included in Summon. E-journal usage increased dramatically and e-book usage alsoincreased for four of six providers examined. Print usage decreased, but the resultswere inconclusive. Conclusions – Summon implementation had a favourable impact on collection usage.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".