Integrated web of youth happiness measures
Bibliographic record
Abstract
The objective of this research is to comprehend how these elements interact to influence young people's subjective happiness and to provide insightful information about their perspectives. Significant discoveries are shown by the study's findings. This research paper conducts a thorough analysis of the many factors that affect youth happiness, including GDP, loneliness, longevity, autonomy, generosity, and corruption. First off, GDP shows up as a significant and noticeably positive factor to young happiness. This emphasizes how crucial economic success is in raising young people's life happiness. Second, autonomy is recognized as another important factor, showing a significant and favorable influence on happiness. It emphasizes how important one's own independence and life control are for young wellbeing. Additionally, the study uncovers a diverse range of impacts among these indicators. Longevity and Generosity are found to positively influence happiness, emphasizing the role of health and social support in young people's contentment. Conversely, Loneliness and Corruption exhibit significant negative effects on happiness, underscoring the detrimental consequences of social isolation and institutional corruption on youth well-being. In conclusion, this research paper offers a holistic view of youth happiness, recognizing the multifaceted nature of its determinants. These findings have important implications for policymakers, highlighting the need to address not only economic aspects but also personal autonomy, social connectedness, and integrity in efforts to promote youth happiness and well-being in society.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".