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Record W2991737592 · doi:10.22329/csw.v16i1.5916

A Critical Discourse Analysis of Haiti Earthquake Recovery in New York Times articles

2019· article· en· W2991737592 on OpenAlexvenueno aff
Loretta Pyles, Juliana Svistova

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsHegemonyAgency (philosophy)AcknowledgementSociologyCritical discourse analysisMeaning (existential)International developmentMedia studiesPrecarityGender studiesPolitical sciencePolitical economyPublic administrationSocial scienceLawPoliticsEpistemologyIdeology

Abstract

fetched live from OpenAlex

The social constructions of the media after the 2010 Haiti earthquake arguably influenced disaster recovery, especially how and what projects were conceived, implemented, and evaluated. In this study of New York Times articles, we sought to learn how Haitians and foreign actors who are engaged in recovery are portrayed in print media. Our findings suggest the presence of hegemonic, disempowering discourse through themes that emphasize the expertise of outsiders and the proliferation of disaster capitalism. A counter-hegemonic, empowering discourse is evident through the acknowledgement of post-colonialist realities and the participation of Haitians in recovery. We discuss the meaning of these findings for social welfare policies, such as those set forth by the United States Agency on International Development (USAID), as well as social work practice and education.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.023
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.006
Science and technology studies0.0130.013
Scholarly communication0.0080.006
Open science0.0010.004
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 head0.027
GPT teacher head0.342
Teacher spread0.314 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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