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Record W4243311102 · doi:10.16993/rl.23

Rebuilding the Landscape of the Rural Post Office: A Geo-Spatial Analysis of 19th-century Postal Spaces and Networks

2016· article· en· W4243311102 on OpenAlexaboutno aff
Nicholas Van Allen, Don Lafreniere

Bibliographic record

VenueRural Landscapes Society Environment History · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Space (punctuation)GeographyRural societyRural communityPhysical spaceOrder (exchange)Rural areaRegional scienceSociologySocioeconomicsPolitical scienceBusinessCartographyComputer scienceLaw

Abstract

fetched live from OpenAlex

This paper uses Post Office (PO) petitions to uncover the complex spatial relationships that developed through the unique social space of the PO. These petitions were signed by the rural people of Middlesex County, Ontario, and submitted to the Postmaster General in order to request changes in the workings of their postal services. When used in a historical GIS they allow us to recreate and reconstitute postal communities in late-19th-century rural Middlesex. By observing the spatial relationships that surrounded the collective requests for changes in postal services, we show how the space of the post office reinforced and helped form rural community and neighbourhood networks. The participation of the post offices users who signed and conducted the petitions is developed at each level of the paper, showing that rural Ontarians were deeply involved in interpreting and altering their own community and neighbourhood landscapes.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0040.006
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.166
Teacher spread0.160 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2016
Admission routes1
Has abstractyes

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