Realigning Teaching To Cultivate Emotional Intelligence in Students
Bibliographic record
Abstract
Building on research that focused on the ‘what’ and ‘why’ of emotional intelligence, this symposium presents four papers from authors representing five countries, that provide empirical evidence on the ‘how’ of enhancing emotional intelligence amongst students. Only then will students be able to successfully navigate geo-political shifts in workers’ identity including unrest at the workplace, mass resignations and intensified labor activism. Through this symposium we hope to encourage teachers to draw on constructivist strategies to create experiential, multi-disciplinary and transformational pedagogies, in which the teacher is both co-learner and role model, while the student is an active and fairly autonomous learner. Teaching to Cultivate Emotional Intelligence in Students Author: Payal Kumar; Indian school of hospitality Author: Tom Elwood Culham; Beedie School of Business Simon Fraser U. Author: Richard Jackson Major; Institut de Gestion Sociale Paris Author: Richard Peregoy; U. of Dallas, Satish & Yasmin Gupta College of Business Author: Chulguen Yang; Southern Connecticut State U. Author: Maria Ivanova; U. of Applied Arts Vienna Author: Ekaterina A. Ivanova; HSE U. Author: Dunia Harajli; Lebanese American U. Author: Bart Norre; U. of Applied Sciences and Arts of Western Switzerland Author: James M Hunt; organisations and the Natural environment Author: Scott N. Taylor; Babson College Author: Lucy Turner; Babson College Author: Danna Greenberg; Babson College Author: Krystal Rawls; California State U., Dominguez Hills Author: Craig Richard Seal; California State U. San Bernardino Author: Sharonda Nicole Bishop; doctoral student at DePaul U. Kellstadt Graduate School of Management Author: Marquis Gardner-N; U. of La Verne Author: Selina Sanchez; California State U. San Bernardino Author: Shammi Gandhi; New Mexico State U.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".