Nine Projects, One Codebase: A Static Search Engine for Digital Editions
Bibliographic record
Abstract
The primary goal of the Endings Project—a collaboration between project leaders, programmers, and librarians to address long term sustainability of digital humanities resources—is to create completely static sites: websites composed of only HTML, CSS, and Javascript that have no reliance on server-side processing and thus, as we have argued elsewhere, stand the best chance in terms of archivability and usability in the long term. Now at the end of the grant cycle, the Endings Project, though successful in its conversion of its past and present projects into static sites, struggled to find a satisfactory solution for replicating the search functionationality necessary for all of our projects. Most search engines require the use of server side processing; though simple Javascript search engines, such as Lunr, do exist, they cannot feasibly handle the large document collections that comprise the standard digital edition. This presentation outlines the creation of staticSearch: an open-access codebase for creating a completely client-side search engine for static websites. A fully open source project, staticSearch enables robust search capabilities for a wide range of digital humanities projects without the need for server-side processing. Built as a collaborative project between UBC's The Winnifred Eaton Archive and eight digital edition projects housed in UVic's HCMC, staticSearch can query any collection of XHTML5 documents and offers advanced searching capabilities, like boolean searches and exact phrase matching, as well as faceted search filters based on configurable document metadata. This presentation discusses the creation of the staticSearch as a multi-project collaboration and how it can offer a robust, future-proof solution for searching across HTML document collections, as well as foster stronger connections between digital humanities resources.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.029 |
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".