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Record W6902638896 · doi:10.7910/dvn/4g4k0

Passed Resolves; Resolves 1837, c.75, SC1/series 228, Petition of Orin Smith

2017· dataset· en· W6902638896 on OpenAlexaboutno aff

Bibliographic record

VenueHarvard Dataverse · 2017
Typedataset
Languageen
FieldSocial Sciences
TopicLegal case studies and regulations
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)EndowmentPoliticsQuarter (Canadian coin)Center (category theory)

Abstract

fetched live from OpenAlex

Petition subject: Gag rule Original: http://nrs.harvard.edu/urn-3:FHCL:10456104 Date of creation: 1837-02-10 Petition location: Middlefield Legislator, committee, or address that the petition was sent to: Green H. Church, Middlefield; committee on that subject Selected signatures: Orin Smith Asa Smith Oliver Smith Total signatures: 34 Legal voter signatures (males not identified as non-legal): 34 Female only signatures: No Identifications of signatories: petitioners, ["others"] Prayer format was printed vs. manuscript: Manuscript Additional non-petition or unrelated documents available at archive: additional documents available Location of the petition at the Massachusetts Archives of the Commonwealth: Resolves 1837, c.75, passed April 12, 1837 Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.

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)0.002
metaresearch head score (Gemma)0.007
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: Dataset · Consensus signal: none
Teacher disagreement score0.398
Threshold uncertainty score0.859

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.001
Scholarly communication0.0070.002
Open science0.0020.003
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.3980.283

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 head0.027
GPT teacher head0.312
Teacher spread0.286 · 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
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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Same venueHarvard DataverseSame topicLegal case studies and regulationsFrench-language works237,207