Factors Influencing Effectiveness of Volunteer Teachers Training in Non-Profit Organizations: Taking Angel Education as an Example
Bibliographic record
Abstract
As the basic unit of composing society, the position and role of education cannot be ignored. With the development of social welfare and growing awareness for basic education, the volunteer teaching is becoming a great mass fervor in China. Many specialized non-profit organizations establish, numerous teaching programs undergo, and countless volunteers throw themselves in rural areas. The devotion of both non-profit organizations and volunteers will have great and far-reaching significance in public welfare. However, as this career is in full swing, problems also shadows. Due to the late start of volunteer teaching service in our country, the research on training theory, practice and legal guarantee is relatively laggard. Although everyone is equipped with enthusiasm for making contribution, their actual performance and teaching achievements are by no means satisfactory. An indispensable reason behind in most non-profit organizations is that training on voluntary teachers is relatively weak at different level, which may be the pivotal intermediate that influences the service result directly. After the research and analysis on a non-profit organization, the writer has found the training in the non-profit organization insufficient in training needs, forms, instructors, contents, evaluations and leadership. Therefore, the obstructive factors influencing the effectiveness of the training should be modified, to improve the service ability of non-profit organizations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".