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Record W4380236476 · doi:10.1515/9780228015208-002

Acknowledgments

2022· book-chapter· en· W4380236476 on OpenAlexaff

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicHuman auditory perception and evaluation
Canadian institutionsAcadia UniversityUniversity of New Brunswick
FundersOmohundro Institute of Early American History and Culture, College of William and MaryAmerican Antiquarian Society
KeywordsComputer science

Abstract

fetched live from OpenAlex

It is a pleasure to acknowledge the personal and intellectual connections that made this project possible.The unusually rich archival sources for Cornwallis Township are found in several repositories, but I owe particular debts to Christine Jack and Leah Grandy in the University of New Brunswick Libraries' Loyalist Collection, and to Patricia Townsend, Wendy Robicheau, and Catherine Fancy at the Esther Clark Wright Archives at Acadia University, all of whom nurture scholarly environments that are welcoming and collaborative.Thanks are also extended to archivists and curators at the Nova Scotia Archives, the Annapolis Heritage Society, the United Church Maritime Conference Archives, and the John Rylands Library in Manchester, u .Long-distance research assistance was provided by the American Antiquarian Society (Worcester, ma) and the Newport Historical Society (ri).The project could not have been completed without the resourcefulness of the University of New Brunswick Document Delivery team and the George A.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Other · Consensus signal: Other
Teacher disagreement score0.406
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5940.379

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 head0.026
GPT teacher head0.217
Teacher spread0.191 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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".

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Citations0
Published2022
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