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Record W6977692345 · doi:10.7910/dvn/jll8e

Massachusetts Archives Collection. v.303-Petitions, 1659-1786. SC1/series 45X, Petition of Jonas Moor

2015· dataset· en· W6977692345 on OpenAlexaboutno aff

Bibliographic record

VenueHarvard Dataverse · 2015
Typedataset
Languageen
FieldMedicine
TopicLegal Cases and Commentary
Canadian institutionsnot available
Fundersnot available
KeywordsArchivistEndowmentService (business)PoliticsCenter (category theory)National archives

Abstract

fetched live from OpenAlex

<p>Petition: Military service </p> <p>Original: <a href="http://nrs.harvard.edu/urn-3:FHCL:13909145">http://nrs.harvard.edu/urn-3:FHCL:13909145</a> </p> <p>Date of creation: 1760-06-12 </p> <p>Petition location: Bolton </p> <p>Legislator, committee, or address that the petition was sent to: Committee </p> <p>Top signatures:<ol><li>Jonas Moor</li></ol> </p> <p>Legislative action: Reported and granted </p> <p>Total signatures: 1 </p> <p>Legislative action summary: Reported, granted </p> <p>Legal v oter signatures (males not identified as non-legal): 1 </p> <p>Identifications of signatories: [males of color?] </p> <p>Prayer format was <a href="http://en.wikipedia.org/wiki/Printing">printed</a> vs. <a href="http://en.wikipedia.org/wiki/Manuscript">manuscript</a>: Manuscript </p> <p>Additional archivist notes: Jonas Moore, Canada expedition, Worcester, Aaron Willard, west river, John Whetcomb </p> <p>Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massa chusetts 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. </p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.008

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 head0.019
GPT teacher head0.258
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes1
Has abstractyes

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