The Role of Emotional Intelligence in Ontario International Graduate Students: An Auto-Ethnography
Bibliographic record
Abstract
Emotional Intelligence (EQ) is a multifaceted ability that helps us to sense, understand, value, and effectively apply the power of emotions as a source of information, trust, creativity, and influence (Goleman, 2006; Salovey, Caruso, & Cherkasskiy, 2011). The five components (self-awareness, self-regulation, empathy, motivation, and social skill) embedded within EQ may work solely or collectively and may individuals cope with everyday life events. Such an emotional tool kit may help multicultural international students to help cope with several adverse situations. The focus of this study was to provide an auto-ethnographic account of a female university student’s experiences as she transitions to become a full-time international graduate student in an Ontario university. The author reflects on the hurdles and socio-emotional challenges experienced during the transition to becoming a graduate student in Ontario. Overall, based on the student’s experiences, findings suggest the need for Canadian universities to incorporate multiple components of EQ into their international university services, including mindfulness, self-regulation, and stress management.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".