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Record W4232753662 · doi:10.32920/ryerson.14668959.v1

Swept to the Shores: An Analysis of Crisis Response Strategies by Canadian Political Leaders in the Aylan Kurdi Crisis

2021· preprint· en· W4232753662 on OpenAlexaffabout
Sara Siddiqi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsToronto Metropolitan University
FundersKasetsart University Research and Development Institute
KeywordsBlameReputationPoliticsTragedy (event)Government (linguistics)DenialPolitical sciencePublic administrationMedia studiesHistorySociologyPsychologyLawSocial scienceSocial psychologyPsychoanalysis

Abstract

fetched live from OpenAlex

Motivation: This MRP explored Stephen Harper and Chris Alexander’s responses to the Aylan Kurdi crisis Canada faced in September 2015. Aylan Kurdi, a three-year old boy, was found dead and photographed on the Turkish shore, close to where he drowned on September 2, 2015. Tima Kurdi, Aylan Kurdi’s aunt, told the media that the family had been trying to come to Canada through a G5 sponsorship agreement but had been denied entry by the Canadian government. Alexander and the Canadian government were criticized – and a crisis resulted. The crisis was particularly important as it came forward during the 2015 Canadian election, when the Conservative government’s refugee policies gained increasing attention. Arguably, this impacted professional image and reputation, as well as Canada’s national reputation. Purpose: The purpose of this MRP is to identify the types of image repair strategies Chris Alexander and Stephen Harper used to respond to the crisis in terms of both professional and national reputation. Methods: Two video responses were selected for examination; they were representative of Harper’s and Alexander’s initial responses to the Aylan Kurdi Crisis. The videos were chosen based on frequency of words such as: crisis, apologize, tragedy, failure, action, and blame. These words have come up frequently in the literature review conducted for this MRP. A content analysis was conducted for this MRP. Both videos were transcribed and coded to determine the types of crisis response strategies used by these leaders. The strategies examined are categorized into four types: denial, evasion of responsibility, reducing offensiveness, and mortification. Descriptors for each category (or sub-strategies under each category) included shifting the blame, defeasibility, bolstering, and apology. All 15 descriptors were drawn from the works of Coombs and Benoit (based on Image Repair Theory and Situational Crisis Communication Theory). To quantify percentages, the entire numbers of crisis responses were divided the number of times a particular crisis response strategy was used. Results: Results indicated that both Harper and Alexander used crisis response strategies of reducing offensiveness, denial, evasion of responsibility, and mortification. Both leaders mainly focused on reducing offensiveness in terms of transcendence, bolstering, corrective action, and performance history. Through an analysis of their responses, it was evident that both leaders addressed Canada’s national reputation. Conclusion: The findings of this MRP present a key area for further exploration in crisis communication: how nations use image repair strategies to restore a tarnished image.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.058
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.009
Science and technology studies0.0130.004
Scholarly communication0.0060.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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