The Status Quo and Strategies of Public-funded Normal Students' Identity of Rural Teachers' Profession
Bibliographic record
Abstract
Teachers' professional cognition is the spiritual cornerstone of students as an excellent teacher. It is the source of the basic strength of the teacher’s career and the most basic psychological preparation for the teacher's career. If we want to cultivate good teachers and normal students, we must first make students appreciate and accept the profession of teachers. Only in this way, students can devote themselves to the position of teacher with a more active and pleasant attitude, relieve all kinds of pressures and problems, strengthen students' identification with the teacher profession, and help students fall in love with this profession. This article researches the status and strategy of the professional identity of rural teachers of publicly funded normal students. After understanding the relevant theories about the professional identity of rural teachers of publicly funded normal students on the basis of literature data, it investigates the current situation of the professional identity of rural teachers of publicly funded normal students. According to the survey results, the professional identity of public-funded normal students at this stage is low, and the basic dimensions of professional identity are all lower than the theoretical median. Among the suggestions for improving professional identity, relevant policies have been introduced to improve the relevant treatment of teachers accounted for 45 %about.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".