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Record W4407941447 · doi:10.3390/socsci14030140

Critical Suicide Notes: On Witnessing and Prefigurative Politics

2025· article· en· W4407941447 on OpenAlexaff
Jeffrey Ansloos, Jennifer White

Bibliographic record

VenueSocial Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychoanalysis, Philosophy, and Politics
Canadian institutionsUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsPoliticsPsychologyCriminologyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

This paper reimagines the study of suicide as a critical, relational practice rooted in solidarity and transformative possibilities. Moving beyond the limitations of conventional suicidology, this work emphasizes the importance of attending to the broader social, political, and structural contexts that shape experiences of suicidality. By framing this work as a collection of “notes,” this paper calls for an approach that notices, marks, and responds to both the violence and resistance inherent in these experiences. This paper introduces witnessing, dreaming, and prefiguration as key methodologies for Critical Suicide Studies. Witnessing is conceptualized as an active and relational practice that centers on the lived realities of those affected by suicide, making their stories and the systems of harm that often go unaddressed, visible. Dreaming involves imagining futures beyond survival, where care and justice guide collective responses. Prefiguration focuses on enacting these futures in the present, embedding relational and community-based approaches in the everyday practices of suicide care and research. Through these practices, this paper explores how Critical Suicide Studies can move from critique to action, creating conditions in which responses to suicide are life-affirming, relational, and grounded in mutual care. This work aspires to cultivate spaces for collective healing, dignity and transformative change.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.100
GPT teacher head0.473
Teacher spread0.373 · 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 designTheoretical or conceptual
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

Citations4
Published2025
Admission routes1
Has abstractyes

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