dbWiz: open source federated searching for academic libraries
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the experiences of developing an open source federated searching tool. It is hoped that it will generate interest not only in dbWiz, but in the many other open source projects either completed or in development at the Simon Fraser University Library and at other libraries around the world. Design/methodology/approach The methods used in this paper include reviewing of related literature, analysis of other federated search tools, and the observation and description of the development process at the Simon Fraser University Library. Findings The paper discusses the benefits and challenges faced in developing an open source federated searching tool for libraries. As a case study, it demonstrates the strength of the collaborative, open source development model. The paper also describes the key features required of any federated searching tool. Originality/value Federated searching is becoming an important new product for both academic and public libraries, with several commercial products to choose from. This paper describes the development of an open source federated search tool that provides a low‐cost, yet highly functional alternative for the wider library community.
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 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".