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Record W2804332317 · doi:10.5539/hes.v8n2p81

A Pragmatic Study of Relational Identity in Bystander Intervention

2018· article· en· W2804332317 on OpenAlexvenueno aff
Xu Huang

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsBystander effectIntervention (counseling)Identity (music)PsychologyPragmaticsIdentity negotiationEmpathyPhenomenonSocial psychologyNegotiationEpistemologySociologyLinguisticsSocial science

Abstract

fetched live from OpenAlex

Given that intervention has been relatively under-researched in pragmatics, this paper offers a linguistic-pragmatic examination of a case of bystander intervention, a notion which is generally known in social psychology. This study approaches the phenomenon of bystander intervention by analyzing discourse data transcribed from a video posted online. Drawing on participation status and relational identity theory, this paper investigates the issues of relational identity and relationships involved in an intervening interaction. The findings indicate that the intervener’s relational identity is in a constant process of construction and negotiation, and the study also notices that three most prominent strategies in our case are employed that give rise to the effective intervention, namely humor, empathy and imposition of power, which might provide some insights into further research in this field.

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)0.021
metaresearch head score (Gemma)0.041
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 score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0090.027
Scholarly communication0.0050.007
Open science0.0010.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.376
Teacher spread0.328 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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