A transcendental phenomenology on existential positive psychology (EPP) and L2 education: Setting a practical agenda for regulating students’ well-being and ill-being
Bibliographic record
Abstract
The contributions of positive psychology (PP) to the study of second and foreign language (L2) emotions have received considerable scholarly attention in recent years. In response to the criticism that it adopted a limited viewpoint, PP has broadened its perspective. Existential positive psychology (EPP) is a branch of PP that can address prior critiques and inform English as a foreign language (EFL) learning, particularly the dialectical relationship between positive and negative emotions. Focus group interview data from eight experienced EFL teachers were analyzed to produce a five-dimensional model, including agendas and sample pedagogical practices. The five layers are “assigning meaningful, relevant, and authentic classroom tasks,” “providing personalized learning paths and plans for learners,” “empowering learner agency, choice, and responsibility,” “admitting and facing existential adversities/feelings,” and “cultivating a culture of positivity and appreciation.” The findings are discussed in light of PP and EPP, and implications are provided for EFL teachers, policymakers, and teacher educators to raise their awareness and knowledge of the EPP contribution to learner emotionality.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".