Determination of Admittance Standards for Teacher Training Institutions: A Delphi Study
Bibliographic record
Abstract
The aim of the study is to determine admittance standard fields and performance indicators for teacher training institutions using the Delphi technique. The expert group of the study consisted of 34 experts fulfilling certain criteria. Delphi technique was utilized in determination of the standards. The technique was completed in three rounds. In the analysis of the data using the Delphi process, descriptive analysis, one of the content analysis methods was conducted during Delphi I. In Delphi II and III rounds, first quarter, median, third quarter and amplitude values were utilized. At the end of the study, the standards for admittance to teacher training institutions were determined within 8 standard areas and 56 performance indicators. Thus, 19 indicators in the field of personality traits, 7 in interest, 1 in health, 3 in field knowledge, 8 in intellectual level, 8 in attitude, 9 in skills and 10 performance indicators in technology standard were determined.
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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 | 0.071 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".