Indicators of Effective Followership for Teachers under the Local Administrative Organization, Thailand: The Structural Relationship Model
Bibliographic record
Abstract
This study aims to examine the consistency of the structural relationship model developed from both related theories and previous studies. Key components, sub-components and related indicators were examined by descriptive method. Sample sizes were controlled by the ratio between sample units and number of parameters as 20:1. A total of 31,026 samples were collected. All samples were teachers who were teaching at schools under the jurisdiction of local administration in Thailand. The questionnaire was used as a 5-level rating scale with 0.979 reliability. Results were based on related hypothesis, WIE model, participatory measurement model (PAR), and critical measurement model (CRT), respectively. The Measurement of Integrity (INT) and FOLL (Good User-Conduct Modeling) models were consistent with those previous studies. The key components, sub-components and indicators were also loaded according to the criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".