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Record W4292226524

Collection Usage Pre- and Post-Summon Implementation at the University of Manitoba

2012· article· en· W4292226524 on OpenAlexaboutno aff
Lisa O’Hara

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceDatabase
DOInot available

Abstract

fetched live from OpenAlex

<b>Objectives</b> – 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.<br><b>Methods</b> – 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.<br><b>Results</b> – 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.<br><b>Conclusions</b> – Summon implementation had a favourable impact on collection usage.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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.

Opus teacher head0.239
GPT teacher head0.463
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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