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Record W2432874325 · doi:10.18438/b81w5x

Library Assessment and Quality Assurance - Creating a Staff-Driven and User-Focused Development Process

2016· article· en· W2432874325 on OpenAlexvenueno aff
Håkan Carlsson

Bibliographic record

VenueEvidence Based Library and Information Practice · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceProcess (computing)Strategic planningProcess managementQuality (philosophy)Plan (archaeology)Computer scienceTracking (education)Engineering managementKnowledge managementBusinessEngineeringService (business)MarketingPsychology

Abstract

fetched live from OpenAlex

Objective – Gothenburg University Library has implemented a process with the goal to combine quality assurance and strategic planning activities. The process has bottom-up and top-down features designed to generate strong staff-involvement and long-term strategic stability.
 
 Methods – In 2008 the library started implementing a system in which each library team should state a number of improvement activities for the upcoming year. In order to focus the efforts, the system has gradually been improved by closely coupling a number of assessment activities, such as surveys and statistics, and connecting the activities to the long-term strategic plan of the library.
 
 Results – The activities of the library are now more systematically guided by both library staff and users. The system has resulted in increased understanding within different staff groups of changing external and internal demands, as well as the need for continuous change to library activities. 
 
 Conclusion – Library assessment and external intelligence are important for tracking and improving library activities. Quality assurance and strategic planning are intricate parts in sustainable development of better and more effective services. The process becomes more effective when staff-driven and built upon systematic knowledge of present activities and users.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.365
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.317
Teacher spread0.266 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Observational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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