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Record W4366549947 · doi:10.1145/3544548.3580868

Governor: Turning Open Government Data Portals into Interactive Databases

2023· article· en· W4366549947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUploadGovernorComputer scienceWorld Wide WebOpen dataGovernment (linguistics)DatabaseTRACE (psycholinguistics)Engineering

Abstract

fetched live from OpenAlex

The launch of open governmental data portals (OGDPs) has popularized the open data movement of last decade. Although the amount of data in OGDPs is increasing, their functionalities are limited to finding datasets with titles/descriptions and downloading the actual files. This hinders the end users, especially those without technical skills, to find the open data tables and make use of them. We present Governor, an open-sourced[17] web application developed to make OGDPs more accessible to end users by facilitating searching actual records in the tables, previewing them directly without downloading, and suggesting joinable and unionable tables to users based on their latest working tables. Governor also manages the provenance of integrated tables allowing users and their collaborators to easily trace back to the original tables in OGDP. We evaluate Governor with a two-part user study and the results demonstrate its value and effectiveness in finding and integrating tables in OGDP.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0070.024
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.007

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.

Opus teacher head0.539
GPT teacher head0.543
Teacher spread0.004 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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