Bibliographic record
Abstract
The purpose of this study was to examine factors that predict optimism and pessimism.Optimism, or positive bias, is the belief that undesirable events are more likely to happen to other people, than to oneself.Pessimism, on the other hand, is the belief that negative life events are more likely to happen to oneself.Although pessimism and optimism are inversely related, they are not opposite ends of the same continuum and should be measured separately.In this study, both dispositional traits (personality) and situational influences (coping styles) were examined in relation to optimism and pessimism.The sample consisted of 178 individuals (M age = 23.00;SD = 6.27; range = 19-50 years; 79% women) who completed an online survey.Participants completed the BFI-2 to assess personality, the Ways of Coping Scale to determine coping styles, and the Future Events Scales to measure optimism and pessimism.The results found a moderate negative correlation between optimism and pessimism, suggesting that although these constructs are related, they are still distinct.A hierarchical multiple regression analysis was conducted using optimism as the criterion variable.The overall model was statistically significant and accounted for 42% of the variance in optimism scores.Significant predictors were lower scores on negative emotionality (neuroticism), and higher scores on extraversion, agreeableness and conscientiousness.As well, problem-focused coping made a unique contribution.Thus, optimists are emotionally stable individuals who are outgoing and sociable, easy to get along with, and responsible.They also are more likely to cope with a stressor by dealing directly with it.A second hierarchical multiple regression analysis was conducted using pessimism as the criterion variable, and again, the overall model was statistically significant, with 36% of the variance accounted for.However, a different pattern emerged with respect to the predictors.In this case, pessimism was predicted by age (being older), gender (being female), and higher negative emotionality (neuroticism) scores.Also, higher scores on emotion-focused coping contributed to the model.Pessimists, therefore, tend to be older and have more life experiences under their belts.They also tend to be women who are more anxious and depressed, and tend to put off dealing with stressors, which may not diffuse the situation.Taken together, these results suggest that our perceptionswhether we have a positive or negative biasare influenced by both dispositional factors (like personality) and situation influences (like coping).
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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 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".