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Reporting Reconnaissance to the Public

2023· book-chapter· en· W4386956585 on OpenAlexaboutno aff
Victoria Sotvedt

Bibliographic record

VenueFordham University Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperSecrecySoftware deploymentPolitical scienceTone (literature)LimitingPublic relationsUnit (ring theory)Identification (biology)AdvertisingComputer securityEngineeringLawPsychologyBusinessComputer scienceArt

Abstract

fetched live from OpenAlex

Armoured reconnaissance units were a relatively new development that first saw extensive use during the Second World War. Because of their intelligence gathering role, newspaper reports on reconnaissance units were sparse and often subject to additional restrictions. Both Canadian and American newspapers navigated these restrictions in highly similar ways, despite writing for audiences that varied significantly in size, social culture, and military organization. These strategies included delaying reports, limiting details that could result in unit identification, and adopting a light-hearted tone that helped obfuscate the bigger picture. Ultimately, the need to provide information to the public and reassure them had to be balanced with secrecy to protect troops and operations in the field. This resulted in the high degree of similarity in reports on reconnaissance units, with the only significant difference being that Canadian newspapers increased the frequency of their reports after units were deployed, while American newspapers carried fewer reports after deployment. The many similarities in reporting styles suggest that closer attention should be paid to how information on combat operations was transmitted to civilians and how this information helped shape popular post-war conceptions of military units and the tasks they carried out.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.309
Teacher spread0.134 · 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.

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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