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Record W6908568183 · doi:10.32469/10355/98846

An examination of the portrayal of homelessness and the opioid crisis in US and Canadian newspapers

2023· dissertation· en· W6908568183 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperFraming (construction)NarrativeJournalismEmpathyThematic analysisQualitative researchNews mediaNarrative inquiryOpioid

Abstract

fetched live from OpenAlex

This research examines how homelessness and the opioid crisis were portrayed from 2018 to 2023 by one US and one Canadian newspaper. The thesis traces qualitative changes in the occurrence of keywords and topics over six years in two newspapers, The Seattle Times and The Vancouver Sun. The data shows prevalence of thematic framing and a shift towards narrative journalism both in The Vancouver Sun and The Seattle Times. The highest use of thematic framing coincided with the onset of the COVID-19 pandemic. The rise in the use of episodic framing appears to coincide with the increase in opioid related fatalities among the homeless population. Over the six years of study, an increasing number of articles started to use personal narratives to discuss the issues of homelessness and the opioid crisis, likely as an attempt to increase attention and empathy for these issues. Study findings highlight how newspapers can influence the public's understanding on the complex issues of homelessness and the opioid crisis.

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)0.003
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 score0.158
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.009
Science and technology studies0.0110.005
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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
Published2023
Admission routes1
Has abstractyes

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