Self-Consciousness Of Teachers In Government Primary Schools: Role Of Social Factors And Economic Challenges
Bibliographic record
Abstract
This study investigates the self-consciousness of teachers in government primary schools, focusing on the interplay of social factors and economic challenges. Self-consciousness, defined as the awareness of one's thoughts, feelings, and behaviors, significantly influences teachers' professional engagement and effectiveness. The research utilizes a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather data from a diverse sample of educators. The findings reveal that social factors, such as community support, peer relationships, and administrative recognition, play a crucial role in enhancing teachers' self-consciousness and, consequently, their job satisfaction. Conversely, economic challenges, including inadequate salaries, lack of resources, and job insecurity, negatively impact teachers' self-perception and professional morale.The study underscores the importance of fostering supportive social environments and addressing economic disparities to promote a positive self-concept among teachers. These insights have implications for policy-makers and educational administrators aiming to enhance the quality of education through improved teacher welfare and professional development initiatives. By highlighting the interconnectedness of social and economic factors, this research contributes to a deeper understanding of the complexities surrounding teacher self-consciousness in the context of government primary education.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".