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Record W2938633052 · doi:10.4324/9781351255912-6

How self-talk promotes self-regulation

2019· book-chapter· en· W2938633052 on OpenAlexaboutno aff
Ariana Orvell, Ethan Kross

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

In this chapter, the authors explain how J. M. Barrie astutely captured a tool that has been empirically shown to aid people in their ability to regulate their behaviors, thoughts, and negative emotions, including those that stem from experiences of social rejection. They outline a theoretical framework that explains why they expect using one’s own name and other non-first-person pronouns should enhance self-regulation, and focus on the role that psychological distance plays in this process. A large body of research suggests that enhancing psychological distance from the self facilitates self-regulation. The authors describe the implications that such “distanced self-talk” has for emotion regulation, and discuss the mechanisms that underlie its benefits. They also describe other linguistic mechanisms that promote psychological distance and discuss the implications of this body of work for interventions aimed at experiences of social rejection. The authors explore the benefits of distanced self-talk for helping individuals regulate their emotions in rejection-relevant situations that elicit social stress.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.006

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 head0.034
GPT teacher head0.309
Teacher spread0.275 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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