Forthcoming in <i>The Modern Language Journal, 110</i> (Supplement 2026)
Bibliographic record
Abstract
The 2026 Supplement is dedicated to a special issue entitled Positive Psychology for Institutions, guest edited by Tammy Gregersen (Baylor University, Waco, Texas), Sarah Mercer (University of Graz, Austria), and Peter MacIntyre (Cape Breton University, Canada). This collection aims to extend the perspective of positive psychology to the institutional dimension. Since its introduction to second language acquisition in the early 2010s, positive psychology has become an influential field within applied linguistics, producing significant research and academic interest. Rooted in three pillars—positive emotions, strengths and character traits, and positive institutions—positive psychology has largely focused on the first two, with little attention to the institutional dimension. This imbalance has drawn criticism for overlooking how systems and institutions shape well-being, potentially placing undue responsibility for happiness on individuals rather than on structural factors. To address this gap, this Modern Language Journal special issue explores positive psychology at the institutional level, examining how schools, universities, and public institutions can foster well-being in language education. The issue adopts an ecological framework, positioning teachers at the center of interconnected systems—individual, social, institutional, and cultural. Through primarily qualitative methods, contributors will analyze how institutional policies, values, and practices support or hinder collective and individual development. This initiative seeks to rebalance positive psychology's focus from individuals to the broader environments that enable positive language learning, offering timely insights in a post-COVID educational landscape.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.430 | 0.224 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".