MétaCan
Menu
Back to cohort
Record W7028636968

Final report and recommendations

2020· article· en· W7028636968 on OpenAlexfundno aff

Bibliographic record

VenueThe South Carolina State Library Digital Collections (South Carolina State Library) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFrench Historical and Cultural Studies
Canadian institutionsnot available
FundersMcMaster UniversityU.S. Department of Commerce
KeywordsGovernorSouth carolinaPlan (archaeology)Advisory committeeWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

On April 20, 2020, Governor Henry D. McMaster created accelerateSC to serve as the coordinated COVID-19 advisory team. The team was created to consider and recommend economic revitalization plans for South Carolina and consists of five total components. While this report does not address every issue or every recommendation necessary for economic revitalization in South Carolina amidst the COVID-10 pandemic, it does seek to identify those issues in need of immediate attention, along with issues that will need to be addresses in South Carolina's continued prosperity.

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)0.017
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.303
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.004
Science and technology studies0.0030.001
Scholarly communication0.0120.009
Open science0.0070.005
Research integrity0.0160.010
Insufficient payload (model declined to judge)0.3030.269

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 head0.032
GPT teacher head0.196
Teacher spread0.164 · 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
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe South Carolina State Library Digital Collections (South Carolina State Library)Same topicFrench Historical and Cultural StudiesFrench-language works237,207