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Record W2891161333 · doi:10.33043/th.35.1.23-27

Working With Probate Inventories: A Class Assignment In Historical Methods

2010· article· en· W2891161333 on OpenAlexaff
Darren Hynes

Bibliographic record

VenueTeaching History A Journal of Methods · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsProbateClass (philosophy)GenealogyHistoryComputer sciencePolitical scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

Probate inventories are listings of property that were sometimes taken upon an individual's death. A team of appraisers would tour the home and lands of the deceased, describing everything of value and assigning an estimated worth. This appraisal would help the administrator or executor of the estate pay off all creditors, with the remainder being distributed according to a will or divided among heirs for those who died intestate. For the past four decades family historians as well as economic and social historians have made extensive use of these inventories, but it is hard to make generalizations about them, as laws governing them vary according to the jurisdiction and year being examined; the literature on probate inventories is consequently very wide-ranging. Used initially for the study of wealth distribution, researchers have also used them to investigate other areas of historical interest. For example, doctors' kits and libraries inform us about the material culture of medicine; word usage and spelling in the lists interest linguists as well as intellectual historians, who also find an indication of the degree of literacy in the number of documents signed by "x" rather than a name; in named books and ownership of Bibles there is evidence of learning and intellectual interests; pottery, dishware, and utensils interest archaeologists; sociologists find status symbols in the details of furnishings, apparel, and cultural objects; agricultural implements tell of farming methods and crop specialization; horses, harness, wagons, and boats indicate modes of production as well as travel; tools speak of craftsmanship; household implements like spinning wheels, wool cards, looms, and soap kettles suggest household production. We can also examine patterns such as the seasonal round of work and the sexual division of labor.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.139
GPT teacher head0.358
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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