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Record W4285387576 · doi:10.1075/cld.21008.slu

Pronoun <i>TA</i> as a facilitator of empathy in Chinese digital narratives

2022· article· en· W4285387576 on OpenAlex
Kerry Sluchinski

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChinese Language and Discourse An International and Interdisciplinary Journal · 2022
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmpathyNarrativePronounFacilitatorPsychologySituational ethicsPerspective (graphical)Relation (database)LinguisticsSocial psychologySociologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This article presents findings pertaining to digital you-narratives in which ungendered third person Chinese pronoun ta is embedded. The study asks what implications the script choice ta, as opposed to gendered 他 ta ‘he’ and 她 ta ‘she’, has for the facilitation of situational empathy when used in conjunction with specific and generic 你 ni ‘you’. The study draws on 131 digital texts from celebrity verified accounts on social media platform Sina Weibo in October 2015. From a Discourse Analytical perspective, the study utilizes pragmatic and textual approaches under a constructivist narrative analysis framework to examine the facilitative role of ta in relation to empathy invoked in readers. The study proposes that ta is a pragmatic resource used to facilitate the co-construction of emergent narratives based on situational empathy. The implications of said findings are discussed along with future avenues of research.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.491

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 communication0.0000.003
Open science0.0010.002
Research integrity0.0000.000
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 head0.006
GPT teacher head0.316
Teacher spread0.309 · 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