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Record W2003778390 · doi:10.1108/10650751111106573

How to choose a free and open source integrated library system

2011· article· en· W2003778390 on OpenAlexaff
Tristan Müller

Bibliographic record

VenueOCLC Systems & Services · 2011
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsBibliothèque et Archives nationales du Québec
Fundersnot available
KeywordsComputer scienceOpen sourceOpen source softwareSoftwareLicenseSet (abstract data type)Open source hardwareSoftware engineeringWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

Purpose This paper seeks to present the results of an analysis of 20 free and open source ILS platforms offered to the library community. These software platforms were subjected to a three‐step analysis, whereby the results aim to assist librarians and decision makers in selecting an open source ILS, based on objective criteria. Design/methodology/approach The methodology applied involves three broad steps. The first step consists of evaluating all the available ILSs and keeping only those that qualify as truly open source or freely‐licensed software. During this step, the correlation between the practices within the community and the terms associated with the free or open software license was measured. The second step involves evaluating the community behind each open source or free ILS project, according to a set of 40 criteria in order to determine the attractiveness and sustainability of each project. The third step entails subjecting the remaining ILSs to an analysis of almost 800 functions and features to determine which ILSs are most suited to the needs of libraries. The final score is used to identify strengths, weaknesses and differentiating or similar features of each ILS. Findings More than 20 open source ILSs were submitted to this methodology, but only three passed all the steps: Evergreen, Koha, and PMB. The main goal is not to identify the best open source ILS, but rather to highlight from which, of the batch of dozens of open source ILSs, librarians and decision makers can choose without worrying about how perennial or sustainable each open or free project is, as well as understanding which ILS provides them with the functionalities to meet the needs of their institutions. Practical implications This paper offers a basic model so that librarians and decision makers can make their own analysis and adapt it to the needs of their libraries. Originality/value This methodology meets the best practices in technology selection, with a multiple criteria decision analysis. It can also be easily adapted to the needs of all libraries.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.007
Science and technology studies0.0050.003
Scholarly communication0.0240.021
Open science0.0030.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.008

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 head0.021
GPT teacher head0.211
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations56
Published2011
Admission routes1
Has abstractyes

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