“One big race”, narrow paths and Golden spoons: fatalistic narratives among young South Koreans
Bibliographic record
Abstract
Durkheim’s Le Suicide outlined two distinct types of suicide that depend on an individual’s level of social regulation. While one of these, anomie/anomic suicide has been greatly explored by both Durkheim and subsequent literature, the concept of fatalistic suicide has been neglected due to Durkheim’s own proclamation that it had little contemporary importance. In this article, I report narratives related to suicide gathered from interviewing South Koreans aged 20–30 that mirror elements of fatalistic suicide, such as violently blocked passions and oppressive discipline. South Koreans in this age group often discussed that they have constantly felt immense pressure from society to achieve particular life goals by certain ages, and not achieving these expectations essentially means that one’s life is over. Furthermore, I contend that achieving these lofty expectations, such as going to what is considered a prestigious university or getting a well-respected first job is hardly possible for the masses, and instead sets up many students and postgraduates for inescapable failure. The reaction to this failure of being able to meet goals and expectations can be understood in terms of Durkheim’s anomie, given that people’s goals can no longer be regulated by society once they have failed. This article posits that individuals move between extremes of Durkheim’s social regulation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".