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Record W2310401313 · doi:10.21810/strm.v4i1.65

Hidden Ties that Bind: The Psychological Bonds of Embedding Have Changed the Very Nature of War Reporting

2012· article· en· W2310401313 on OpenAlexaffvenue
Sherry Wasilow

Bibliographic record

VenueStream Interdisciplinary Journal of Communication · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsCarleton University
Fundersnot available
KeywordsFraming (construction)Perspective (graphical)StorytellingPsychologyPublic relationsPolitical scienceSocial psychologySociologyHistoryNarrativeLiteratureArtVisual arts

Abstract

fetched live from OpenAlex

While embedded reporting has been allowed entry into the ongoing chess game between the military and the media, it is not apparent that the psychological nuances of its origins and impact on storytelling have been fully explored. Yet its adoption as a viable option in war reporting has led to a de facto division of journalistic framing. Embedding reporters with troops has led to a micro focus on soldiers and the minutiae of conflict, while traditional or unilateral reporting continues to provide contextual perspective on the purpose and impact of the fighting within a bigger picture. This essay argues that this division needs to be bridged.

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)0.017
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.048
Scholarly communication0.0190.027
Open science0.0010.015
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.078
GPT teacher head0.430
Teacher spread0.353 · 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 designQualitative
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

Citations1
Published2012
Admission routes2
Has abstractyes

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