Investigation of the Factors That Effect the University Students’ Desperation Levels (Kafkas University Example)
Bibliographic record
Abstract
The desperation is a kind of negative foresight on the contrary of positive foresight for the future. That is to say, to some extend, and it is an emotional situation having negative expectations for the future. The aim of the study, determination the factors that are affecting the university students’ desperation levels. This study was conducted to examine the students of Kafkas university despite levels for the future. The sampling groups of this study were the students of Kafkas University Education Faculty educated at Physical Education and Sports Department (25 students), Basic Mathematics Teaching Department (25 students), Science Teaching Department (25 students), and Social Studies Teacher’s Department (25 students) 4th class totally 100 participant students in 2016-2017 academic year. The sampling group was selected using simple random—the data handed with the help of the data collecting scale evaluated by using the SPSS Package Program. The preferences are “Yes (Correct) and No (Wrong)”. As a result of this study, there wasn’t a meaningful difference in the participant students’ desperation levels according to the variables. Students’ desperation level means were lower than p < 0.05.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".