Samomor v Avstrijskem primorju na prelomu 19. in 20. stoletja
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".