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Record W2788978911 · doi:10.1184/r1/6622118

Equal Time for Data on the Internet with WebSemantics

2018· article· en· W2788978911 on OpenAlex
George A. Mihaila, Louiqa Raschid, Anthony Tomasic

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.

Bibliographic record

VenueResearch Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceData exchangeData sharingSchema (genetic algorithms)The InternetSemantics (computer science)World Wide WebData publishingData integrationData discoveryData scienceInformation retrievalPublishingDatabaseMetadataProgramming language

Abstract

fetched live from OpenAlex

Many collections of scientific data in particular disciplines are available today around the world. Much of this data conforms to some agreed upon standard for data exchange, i.e., a standard schema and its semantics. However, sharing this data among a global community of users is still difficult because of a lack of standards for the following necessary functions: (i) data providers need a standard for describing or publishing available sources of data; (ii) data administrators need a standard for discovering the published data and (iii) users need a standard for accessing this discovered data. This paper describes a prototype implementation of a system, WebSemantics, that accomplishes the above tasks. We describe an architecture and protocols for the publication, discovery and access to scientific data. We define a language for discovering sources and querying the data in these sources, and we provide a formal semantics for this language.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0000.002
Open science0.0090.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.174
GPT teacher head0.321
Teacher spread0.148 · 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