Investigando las fortalezas personales para crear vidas y ambientes positivos: una perspectiva internacional
Bibliographic record
Abstract
In today’s world, we face a barrage of difficulties in multiple life spheres. While previous times were not without their challenges, these were often restricted to our own personal, geographically localized worlds. Today, news and social media expose us to never-ending reports of traumatic events and interpersonal violence, making us question human nature and our resiliency. Increasing technological advancements have brought forth new challenges, not only in our relationships with technology, but in how we live our daily lives. Financial uncertainty at both the individual and global level raises doubts about our abilities to afford basic necessities. Climate change is wreaking havoc on the environments we call home. Changing interpersonal dynamics present new challenges to personal, social, and group relationships, often resulting in conflict or isolation. Adolescents and young adults are thrust into this confusing world, often lacking the proper resources to understand and cope with these challenges. Adults facing life’s demands also experience extreme stress, with adverse consequences both at the present as well as later life in the form of physical and mental health issues. Furthermore, we have a tendency to direct attention to our individual weaknesses, exacerbating our experience of difficulties. It is, therefore, no wonder that psychology as a discipline, which seeks to understand the human experience, tends to focus on the deficiencies in our lives. However, as stated by Sheldon and King (2001), it is important for psychologists to deviate from this ‘negative bias’, and instead concentrate on positive human qualities and the promotion of what Maslow (1943, 1987) termed growth needs. This focus is the crux of positive psychology.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".