�Empathy Scaling and Its Impact on Employee�s Eustress� - A Study With Special Reference to Autonomous Colleges in Mangalore
Bibliographic record
Abstract
In order to formulate a parsimonious tool to assess empathy, a self-report measure named Toronto Empathy Questionnaire (TEQ) is used. It demonstrates clearly the strong convergent validity, correlating positively with behavioural measures of social decoding exhibiting a good internal consistency and high test-retest reliability. In order to reach at accurate research conclusions, questions were re-worded to assess frequency of behavior rather than to pose general statements or tendencies. Responses were collected from a sample of hundred teachers from autonomous colleges in Mangalore city, and performances were ranked using a 5-point Likert-scale corresponding to various levels of frequency (i.e., never, rarely, sometimes, often, always. As the stress increases, we become less able to solve the real problems, costing billions of dollars, reducing the quality of life, driving economic meltdown and even destroying the environment (Distress). Hence, emotional competencies have proven to contribute more towards workplace productivity through the cognitive and social development of an individual (Eustress). DOI: 10.17762/ijritcc2321-8169.150205
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".