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Record W4308076653 · doi:10.54590/pop.2022.005

Harmonizing Open Licenses among Online Databases of Enthusiast Communities: Challenges for the Legal Integration of Databases in the Japanese Visual Media Graph Project

2022· article· en· W4308076653 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePop! Public Open Participatory · 2022
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLicenseGlobeComputer scienceDatabaseGraphWorld Wide WebOpen dataOrder (exchange)Social mediaGraph databaseOpen sourceData scienceBusinessSoftware

Abstract

fetched live from OpenAlex

The Japanese Visual Media Graph project has created a knowledge graph for researchers working on popular Japanese visual media by combining data compiled by various enthusiast online communities. In order to open up the knowledge graph to researchers around the globe, the databases needed to be integrated legally. This article discusses the hurdles we encountered in this integration process and the solution we settled on to overcome these problems. We provide a brief look at the complexity of the legal protection afforded to databases, which we found to be an important source of problems even for communities that attempted to apply appropriate open licenses to their data. Finally, we detail how using the CC BY-NC-SA 4.0 license as a smallest common denominator and asking communities to provide us with a separate tailor-made licensing agreement helped address both the concerns of the communities and the long-term needs of the project.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0050.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.444
GPT teacher head0.430
Teacher spread0.014 · 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