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Record W7094726139

Samomor v Avstrijskem primorju na prelomu 19. in 20. stoletja

2024· article· en· W7094726139 on OpenAlexaboutno aff

Bibliographic record

VenueDiRROS repository (University of Maribor) · 2024
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperQuarter (Canadian coin)Suicide preventionPerceptionPoison controlSocial issuesPeriod (music)Suicide methods
DOInot available

Abstract

fetched live from OpenAlex

The paper seeks to examine the social image of suicide at the end of the 19th and the beginning of the 20th century, focusing on the case of Trieste as a major Austrian urban centre, where the suicide rates were visibly increasing from at least the 1870s onwards. The perception of the growing presence of suicide in society made it possible to observe the reflec-tions on suicide from the last quarter of the 19th century, originating from different parts of Europe (Morselli, Masaryk, Durkheim), and at the same time how the discourses around suicide shed light on a somewhat broader picture of society, including its fears (of social problems and change, not least the potential threat of the imitative effect that the daily press was believed to create by reporting on suicides).The newspaper discourse usually followed the scientific publications of the time, and the contemporaneous observations on the mass of suicides were confirmed through statistical analyses and medical, sociological, philosophical, and other debates, while raising many other social issues with which suicide in urban areas could be linked (alcoholism, the growth of the proletariat, poverty, changing values, etc.). All these factors shaped the public debate on suicide as a problem of modern society, with an emphasis on (big) cities, where the prob-lem of suicide was much more pronounced than in smaller, non-industrial towns, or in the countryside of the Austrian Littoral.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.675

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 studies0.0000.000
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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