From positive psychology to existential positive psychology (EPP): New directions for theory, research, and practice
Bibliographic record
Abstract
For years, research on psychology was dominated by negative emotions and stressors in life and academia. However, with the second wave of positive psychology (PP) and the emergence of existential positive psychology (EPP), an approach of polarity highlighting both positive and negative emotions came into vogue in educational psychology. The literature on EPP in second/foreign language (L2) education is in its initial stages and requires more research. To shed light on the importance of moving from PP to EPP, the present conceptual study makes an effort to explain the theoretical basis of EPP and how it can be incorporated into theory, research, and practice in L2 education contexts. The definitions, models, waves, theories, and future directions are eloquently explicated in this theoretical study. It is also suggested that EPP can make several contributions to the theory, research, and practice of L2 education. Theoretically, EPP informs PP, L2 identity, and motivation theories and models, especially in the age of anthropogenic changes. Practically, our conceptual paper assists L2 teachers and learners in the detection and regulation of existential challenges and the all-too common negativities in the L2 learning process. In terms of research, a list of uncharted territories pertaining to Artificial Intelligence (AI), monomodal and multimodal AI technologies, complex dynamic system theory, and the ecological perspective of EPP themes is provided. Our paper advances research and understanding of EPP as a bridge toward cultivating growth and well-being among L2 educators through life adversities.
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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 | 0.039 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.102 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".