Educators' Role in Fostering Self-Esteem in Their Students to Promote Achievement
Bibliographic record
Abstract
In an age of extreme technological and media exposure, children are bombarded with subliminal indoctrination of messages which place undue value on outward appearance. This results in constant disappointment and dissatisfaction with one's self because it is impossible to compare or keep up with the false ideals and unattainable expectations that which society sets. When a child experiences a decrease in self-esteem, he begins to question his self-worth. Further, if unequipped with adequate social and life skills, such as resiliency or self-efficacy, he begins to doubt his abilities and his potential for success. Once his self-confidence is lost, he descends into a state of perpetual doubt of self-worth and self-love; thus, partaking in a cycle which is difficult to break. In turn, educators must implement prevention tactics for students to become more prepared to face inevitable challenges. This qualitative research study seeks to investigate the various perceptions, pedagogies, and experiences of two primary and junior teachers from Ontario and Quebec who aim to tackle this recurring phenomenon. Through a thorough literature review in collaboration with two qualitative interviews, I aim to explore the ways in which educators implement pedagogies that help foster self-esteem in their students. The findings of this research should provide educators with practical and transferrable strategies that can support students' growth and success in all aspects of their lives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".