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GENDERED COMMUNICATION AND MANIPULATION: A COMPARATIVE ANALYSIS OF FEMALE SPEECH IN THE ADVERTISING INDUSTRY

2023· article· en· W4390873259 on OpenAlexaboutno aff
Elena N. Malyuga, Anna A. Khaperstkova

Bibliographic record

VenueSWS International Scientific Conference on Arts and Humanities · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsAppealAppeal to emotionNarrativeAdvertisingQualitative analysisQualitative researchPsychologyContent analysisPublic relationsSociologyPolitical scienceLinguisticsBusinessSocial science

Abstract

fetched live from OpenAlex

This study aims to identify and assess the manipulative potential of language used in corporate culture through an analysis of female employees' speech in Canadian and British advertising agencies. By addressing this research gap, the study seeks to answer the question of how gender influences the use of manipulative strategies in advertising. To investigate this question, the study employs a comparative research design, complemented by a qualitative analysis of the findings. The research material includes business speeches, presentations, interviews, and articles featuring narratives produced by female respondents working in Canadian and British advertising agencies. The findings suggest that women use emotional and persuasive language to appeal to clients and create a positive attitude towards advertised products. Study results outline specific linguistic differences between Canadian and British female advertising professionals in terms of their use of manipulative language strategies.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.256
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0070.004
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.314
GPT teacher head0.408
Teacher spread0.094 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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