Using Character Strengths to Address English Writing Anxiety
Bibliographic record
Abstract
Positive psychology has been introduced to the applied linguistics literature with the broad goal of improving the experience of language learners and teachers through a variety of interventions (MacIntyre & Mercer, 2014; Gabryś-Barker & Gałajda, 2016). “The aim of positive psychology is to catalyze a change in psychology from preoccupation only with repairing the worst things in life to also building the best qualities in life” (Seligman &Csikszentmihalyi, 2000, p. 5). One significant contribution of this young field has been a series of empirically-tested positive psychology interventions (PPIs) that have been shown to increase positive emotion, reduce distress, and/or improve well-being (Seligman, Steen, Park, & Peterson, 2005; Sin & Lyubormirsky, 2009). In the present research, we examine one application of a PPI involving a focus on using character strengths as a way to address language anxiety. Through a case study analysis, we demonstrate the ways that this intervention was beneficial for the student.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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 teacher head, 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".