Bibliographic record
Abstract
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.
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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 | 0.029 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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.
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".