MétaCan
Menu
Back to cohort
Record W3174673265

"Canada Can Do Better": An Exploration of Canadian News Media's Portrayal of Federal Penitentiaries and Prisoners During a Global Pandemic.

2021· article· en· W3174673265 on OpenAlexaboutno aff
Ihsan Hage-Hassan

Bibliographic record

VenueSFU Undergraduate Research Symposium Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperCriminal justiceDepictionEconomic JusticePandemicCriminologyPolitical scienceNews mediaPrisonPublic relationsCoronavirus disease 2019 (COVID-19)SociologyLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

COVID-19 has impacted everyone on a global scale and Canada is not immune. Canadian prisoners have faced many challenges and the media have been quick to document COVID-19 in justice facilities. How the media document these events plays a crucial role in our understanding of the criminal justice system. Therefore, it is imperative that researchers understand the media’s depiction of these atrocities because not only does it influences the public’s knowledge and understanding of the criminal justice system, but it has the potential to create policy change. Prior research on this subject matter has found that the media consistently offer an inaccurate and misconstrued image of prisoners, as being extremely violent and dangerous, and prisons being akin to a hotel. The current study adds to the existing literature by examining how online Canadian newspaper articles portray federally sentenced prisoners and the institutions they reside in during the pandemic. Conducting a content analysis of 85 articles published from a March 2020 to January 2021, the researcher sought emergent themes. The findings, limitations, suggestions for future research, and policy recommendations are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.358
Teacher spread0.283 · 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.

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 routes1
Has abstractyes

Explore more

Same venueSFU Undergraduate Research Symposium JournalSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207