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Record W7110154474 · doi:10.5281/zenodo.17850205

Open Politics

2025· article· W7110154474 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldSocial Sciences
TopicScience and Science Education
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsPoliticsSupreme courtOpen governmentLegislatureEconomic JusticePower (physics)Order (exchange)Public interestAudit

Abstract

fetched live from OpenAlex

Open Politics is the application of open source governance best practices in the scientific methodology public policies secure operations development (DevSecOps). It's being researched through my decades of works as an apartisan neutral technical legislative task forces, supreme courts amicus curiae, and executive power curatorial jury analyst and compiled for a scholar consultancy for the Brazil Federal Public Ministry (MPF) department of justice to be vastly reviewed for the Brazil Supreme Military Court (SCM) and Order of Lawyers of Brazil (OAB) 'Military Law' Summit. Its openly disclosed interest is the establishment of more transparently audited open houses of policymaking, even if the creation of an Open Party is needed for that.

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.082
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.010
Scholarly communication0.0160.013
Open science0.0010.009
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0820.033

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 head0.085
GPT teacher head0.361
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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