A Teacher Accountability Model for Overcoming Self-Exclusion of Pupils
Bibliographic record
Abstract
Self-exclusion of pupils is one of the prominent challenges of education. In this paper we propose the TERA model, which shapes the process of creating formative accountability of teachers to overcome the self-exclusion of pupils. Development of the model includes elaboration and integration of interconnected model components. The TERA model involves the following components: Tasks, Environment, Reward, and Accountability. The “Task” component serves as the starting point for creating teacher accountability. The objective of the Task” component is to form a structure of tasks, performance of which leads to overcoming self-exclusion of pupils. The task structure allows determining task significance for pupils’ inclusion. The “Environment” component organizes an environment inducing the teachers toward productive and qualitative performance of the tasks directed toward overcoming self-exclusion of pupils. Such an environment fosters creating teacher reciprocal accountability for task performance results. The environment is organized by the conditions of adaptive performance of the tasks and adaptive rewarding of teachers. “Accountability” is the central component of the model. The objective of this component is to create formative accountability of teachers for task performance results. It is attained through self-assessment willingness, possibility, and desire of teachers to be emergent leaders or performers during tasks performance, and coordination of the self-assessment outcomes. The objective of the “Reward” component is to realize adaptive rewarding of teachers for taking accountability for task performance results. Adaptation of rewards is provided relative to task significance and a teacher’s role in a task’s performance.
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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 | 0.001 | 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".