Enhancing Access Across Europe for Documents Published According to Freedom of Information Act: Applying Woogle Design and Technique to Estonian Public Information Act Document
Bibliographic record
Abstract
In the Netherlands, the Open Government Act (Wet openbare overheid or Woo/Wob in Dutch) is in effect, with the primary objective of ensuring a more transparent government. In line with the legislation, a search engine named Woogle has been designed and developed to centralize documents published under the Open Government Act. The Estonian Public Information Act serves a similar purpose and requires all public institutions to publish information generated during official duties, fostering transparency and public oversight. Currently, Estonia’s document repositories are decentralized, and content search is not supported, which hinders people’s ability to efficiently locate information. This study aims to assess public information accessibility in Estonia and to apply Woogle’s design and techniques to Estonia’s document repositories, thereby evaluating its potential for broader European implementation. The methodology involved web scraping data and documents from 57 Estonian public institutions’ document repositories. The results indicate that Woogle’s design and techniques can be implemented in Estonia. From a technical perspective, the alignment of the fields was successful, while it was found that content-wise, the Estonian data present challenges due to inconsistencies and lack of comprehensive categorization. The findings suggest potential scalability across European countries, pointing to a broader applicability of the Woogle model for creating a corpus of Freedom of Information Act documents in Europe. The collected data are available as a dataset.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".