Bibliographic record
Abstract
In this research, publications on "well-being" between 1993 and 2022 in the Web of Science database \nare examined. The bibliometric analysis technique was used in the study. As a result of the \nbibliometric analysis of 2390 articles evaluated, the following data were obtained: The year with the \nmost written articles was 2021. It has been observed that there has been an increase in the number of \narticles since 2008. The four authors most cited were Ryff, Diener E., Ryan, and Seligman. The top \nfour institutions cited are University College London, Melbourne University, Sydney University, \nMonash University and Oxford University. The top four publishing institutions are as follows, \nrespectively. Melbourne University, University College London, Monash University and Sydney \nUniversity. The four most cited countries are the USA, the UK, Australia and the Netherlands, while \nthe four most publishing countries are the UK, USA, Australia and Spain. The top four most cited \njournals are Personality and individual differences, Ageing & Society, Plos One, and Frontiers in \nPsychology, respectively. The top four journals with the most publications are as follows: Frontiers in \nPsychology, International Journal of the Environment, Plos One and Personality and individual \ndifferences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.044 | 0.055 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".