The Development and Validation of Self-Concept Scale For Pakistani University Students
Bibliographic record
Abstract
Cultural aspects are dominant in almost all areas of research and require special attention while conducting a study. The same goes for under-focused self-concept which varies from individualistic to collectivistic cultures. In the present research, an indigenous scale was developed to explore the self-concept of university students in Pakistani culture. In the first phase, 60 university students were interviewed separately and generated the item pool of 46 statements. Then repeated and ambiguous items were excluded, and a list of 40 items was used for piloting on 30 university students as a self-report measure of a 5-point rating scale (Self-Concept Scale). Finally, a convenient sample of 300 university students (154 boys and 146 girls) was given the final list of 40 items, Self-Concept Scale for Adolescents, and a demographic sheet. The Statistical Package for Social Sciences (SPSS) was used to investigate the collected data. The Exploratory Factor Analysis (EFA) produced a two-factor solution: positive and negative self-concept. Lastly, 38 items were finalized for the self-concept scale, the first factor was based on 22 items and the second factor consisted of 16 items. The SCS was found to have high internal consistency, concurrent validity, and split-half reliability. This scale can be used in further research, assessment, and counseling services for the students.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".