MétaCan
Menu
Back to cohort
Record W4398842502 · doi:10.7910/dvn/xkjdwu

Petition of Thomas Sawin

2018· dataset· en· W4398842502 on OpenAlexaboutno aff
Massachusetts Archives Digital Archive Of Native American Petitions

Bibliographic record

VenueHarvard Dataverse · 2018
Typedataset
Languageen
FieldSocial Sciences
TopicLegal case studies and regulations
Canadian institutionsnot available
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

Petition subject: Property Original: http://nrs.harvard.edu/urn-3:FHCL:26855789 Date of creation: 1695-02-27 Petition location: Sherborn Selected signatures: Thomas Sawin Actions taken on dates: 1695-03-14,1695-06-15,1695-06-22 Legislative action: Read and voted for hearing in the House on March 14, 1695 and sent for concurrence read and voted for hearing at the next session in the House on June 15, 1695 and sent for concurrence and concurred in the Council on June 22, 1695 Total signatures: 1 Legislative action summary: Read, hearing, sent, read, hearing, sent, concurred Legal voter signatures (males not identified as non-legal): 1 Female only signatures: No Prayer format was printed vs. manuscript: Manuscript Native American tribe: Nipmuc 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. Additional archivist notes: attorney, Natick Indians, request that 1700 acres of land be returned to the Indians by Samuel Gookin and Samuel Howe, survey, Cambridge, Sudbury, lands, sale, Matthew Rice, Penn Townsend, Nehemiah Jewett, William Bond, Isaac Addington, David Fisk Location of the petition at the Massachusetts Archives of the Commonwealth: Massachusetts Archives volume 30, pages 361-361a

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)0.001
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.193
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.1930.080

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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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
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
Published2018
Admission routes1
Has abstractyes

Explore more

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