The relationship between employees’ self-esteem and pertinacity
Bibliographic record
Abstract
The purpose of this paper is to evaluate the relationship between employees' of East Azarbaijan Melli bank (zone 10) self-esteem and pertinacity.For this reason, employee's self-esteem was arranged in two dimensions, which are consistency solidity, emotional inconsistency and pertinacity.The questionnaire is based on Kobasa theory including three sides including commitment, control and defiance.There are two basic and three subsidiary theories.Employee of East Azarbaijan Melli bank (zone 10) is statistical society of this research, which includes 80 people.Reference to restricted volume of statistical society, total statistical society is concerned as under evaluation society.The tool of data gathering is two questionnaires, which are Aizenc's self-esteem questionnaire and Kobasa's pertinacity standard questionnaire, which are delivered for evaluating society after perpetuity and justifiability determination.The descriptive statistical methods are used for collected questionnaires analyze.Thus, the descriptive statistical method was used to summarize, to categorize and to interpret statistical data's.In addition, statistical tests such as Pearson and Freidman's coherency R are used to test the hypothesis of research.The results indicate that there is a meaningful relationship between self-esteem and pertinacity and its sides on employees of East Azarbaijan Melli bank (zone 10).They present maximum relationship between self-esteem and pertinacity control and minimum relationship between pertinacity commitment dimensions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".