C. LOMBROSO: ON THE QUESTION OF SUICIDAL CHARACTERISTICS IN FEMALE LOVE
Bibliographic record
Abstract
The feeling of love, in particular romantic love, is ambivalent in nature, and therefore can lead to both constructive and destructive actions and deeds (this refers to “freedom for” and salvation, on the one hand, and “freedom from” and self-destruction (completed suicides and suicide attempts), on the other. Unfortunately, according to statistics, the vast majority of suicides due to tragic love affairs are committed by women of all ages. In view of the above, the aim of this study is to clarify the essence of such a suicidal cause as love and its impact on the suicidal activity of women. The article is based on the scientific research of C.Lombroso, E.Durkheim, and E. Fromm, which needs to be rethought in light of the actualization of gender equality issues in the second quarter of the 21st century. The paper analyzes cases of suicide among women due to both extramarital affairs and divorce (marital anomie, according to É.Durkheim’s terminology). Today, the opinion of C.Lombroso deserves attention, according to which the number of suicides due to love is higher among women who are not married but are in extramarital relationships dominated by passion. The scientific research conducted in the field of suicidology has allowed us to identify several important points. 1. The nature of true love, such as maternal love, is constructive, allowing women to perform salvific and restorative functions aimed at achieving “freedom for,” searching for meaning, preserving the family/self-preservation. 2. The nature of passionate love, in which a woman dissolves into another, plunging into voluntary slavery, which is equivalent to “freedom from” herself, is destructive, aimed at destruction and self-destruction (suicide). 3. The suicidal feature of female love (unhappy, betrayed love) lies in forgiving the object of love, merging with them in death. In the future, this study can serve as a theoretical basis for further scientific research in the field of suicidology, as well as a methodological basis for the development of recommendations and suicide prevention programs for women of all ages who have suffered painful losses, in particular due to unhappy love. The development of such programs is important at all levels, from the national to the local (individual cases), and should become one of the strategies of domestic social policy implemented by social workers in communities across Ukraine.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".