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Record W4308246561 · doi:10.54590/pop.2022.003

Open, Collaborative Commons: Web3, Blockchain, and Next Steps for the Canadian Humanities and Social Sciences Commons

2022· article· en· W4308246561 on OpenAlexvenueaboutno aff
Talya Jesperson, Graham Jensen, Caroline Winter, Alyssa Arbuckle, Ray Siemens

Bibliographic record

VenuePop! Public Open Participatory · 2022
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCommonsScholarshipOutreachBlockchainCommercializationDigital humanitiesPolitical scienceCorporate governanceAccountabilityThe InternetPublic relationsLibrary scienceBusinessWorld Wide WebComputer scienceComputer securityLaw

Abstract

fetched live from OpenAlex

This paper provides an update on the Canadian Humanities and Social Sciences (HSS) Commons, an in-development online hub for open social scholarship in Canada and beyond, and considers the next steps for the platform in an ever-evolving digital landscape. It outlines various recent outreach and engagement events intended to introduce the Canadian HSS Commons to the larger communities to which it belongs. Because the Canadian HSS Commons is committed to supporting the growth and evolving needs of these communities, this paper also considers how increasingly popular internet technologies such as Web3 and blockchain might play a part in the future of digital research infrastructure and the Canadian HSS Commons specifically. It concludes that while Web3 and blockchain currently raise important questions and concerns about governance, accountability, and commercialization, in the near future, these same technologies could also help engender new forms of functionality and participation on the Commons.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, 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 score0.998
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0140.013
Scholarly communication0.0160.013
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.515
GPT teacher head0.441
Teacher spread0.074 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

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