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Record W3008079375 · doi:10.1177/0956797620902380

Thinking of You: How Second-Person Pronouns Shape Cultural Success

2020· article· en· W3008079375 on OpenAlexaff
Grant Packard, Jonah Berger

Bibliographic record

VenuePsychological Science · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsYork University
Fundersnot available
KeywordsPersonal pronounPsychologyMeaning (existential)NarrativeSituatedFeelingPerspective (graphical)Object (grammar)Context (archaeology)Social psychologyThe artsLinguisticsVisual arts

Abstract

fetched live from OpenAlex

Why do some cultural items succeed and others fail? Some scholars have argued that one function of the narrative arts is to facilitate feelings of social connection. If this is true, cultural items that activate personal connections should be more successful. The present research tested this possibility in the context of second-person pronouns. We argue that rather than directly addressing the audience, communicating norms, or encouraging perspective taking, second-person pronouns can encourage audiences to think of someone in their own lives. Textual analysis of songs ranked in the Billboard charts ( N = 4,200), as well as controlled experiments (total N = 2,921), support this possibility, demonstrating that cultural items that use more second-person pronouns are liked and purchased more. These findings demonstrate a novel way in which second-person pronouns make meaning, how pronouns’ situated use (object case vs. subject case) may shape this meaning, and how psychological factors shape the success of narrative arts.

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)0.006
metaresearch head score (Gemma)0.027
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 score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.192
GPT teacher head0.362
Teacher spread0.170 · 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

Citations50
Published2020
Admission routes1
Has abstractyes

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