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Record W3015192810 · doi:10.5539/ells.v10n2p17

An Analysis of Boeing’s Trust-Repair Discourse

2020· article· en· W3015192810 on OpenAlexvenueno aff
Guihang Guo, Chengfei Mi

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsTrustworthinessPublic relationsDialogicShareholderAsset (computer security)Corporate communicationBusinessSociologyPolitical scienceManagementCorporate social responsibilityCorporate governancePsychologyEconomicsSocial psychologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

Trust is a valuable asset for business organizations and is vital for an organization’s survival. After two air clashes, Boeing is facing trust crisis and is making great efforts to earn trust back. Corporate communication is a common and important means for the management to demonstrate the company’s attitude and to persuade the audience that they are reliable. This paper aims to elucidate the role of corporate communication in recovering the damaged trust of Boeing. The paper applies the model of trust-repair discourse established by Fuoli and Paradis to analyze Boeing’s CEO’s address to shareholders following the two fatal crushes. It is found that the CEO repairs the company’s trustworthiness from three aspects, namely, ability, integrity and benevolence. To fulfill this purpose, the CEO adopts such strategies as neutralizing the negative and emphasizing the positive. Through the use of evaluating and dialogic engagement resources, the CEO discursively rebuilds and renegotiates the company’s trustworthiness.

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)0.001
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 score0.034
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.351
Teacher spread0.336 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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