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Record W4384501527 · doi:10.3386/w31468

The Electric Telegraph, News Coverage and Political Participation

2023· report· en· W4384501527 on OpenAlexaff
Tianyi Wang

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsPolitical scienceTelecommunicationsAdvertisingComputer scienceBusinessLaw

Abstract

fetched live from OpenAlex

Using newly digitized data on the growth of the telegraph network in America during 1840-1852, the paper studies the impacts of the electric telegraph on national elections.I use proximity to daily newspapers with telegraphic connections to Washington to generate plausibly exogenous variation in access to telegraphed news from Washington.I find that access to Washington news with less delay had a robust positive effect on voter turnout in national elections.For mechanisms, I provide evidence that newspapers facilitated the dissemination of national news to local areas.In addition, text analysis on more than a hundred small-town weekly newspapers from the 1840s shows that the improved access to news from Washington led newspapers to cover more national political news, including coverage of Congress, the presidency, and sectional divisions involving slavery.The results suggest that the telegraph made newspapers less parochial, facilitated a national conversation and increased political participation.I find little evidence that access to telegraphed news from Washington affected party vote shares or Congressmen's roll call votes.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.542
GPT teacher head0.613
Teacher spread0.071 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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