Ligo: An Open Source Application for the Management and Execution of Administrative Data Linkage
Bibliographic record
Abstract
IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process.
 Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner.
 ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after.
 Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".