An assessment of the curriculum and implementation of the subject Religious and Moral Education in Namibia: A case study of perceptions of Religious and Moral Education teachers in the Ompundja Circuit of Oshana Region
Bibliographic record
Abstract
Previously learned reward values can have a pronounced impact, behaviorally and neurophysiologically, on the allocation of selective attention. All else constant, stimuli previously associated with a high value gain stronger attentional prioritization than stimuli previously associated with a low value. The N2pc, an ERP component indicative of attentional target selection, has been shown to reflect aspects of this prioritization, by changes of mean amplitudes closely corresponding to selective enhancement of high value target processing and suppression of high value distractor processing. What has remained unclear so far is whether the N2pc also reflects the flexible and repeated behavioral adjustments needed in a volatile task environment, in which the values of stimuli are reversed often and unannounced. Using a value-based reversal learning task, we found evidence that the N2pc amplitude flexibly and reversibly tracks value-based choices during the learning of reward associated stimulus colors. Specifically, successful learning of current value-contingencies was associated with reduced N2pc amplitudes, and this effect was more apparent for distractor processing, compared with target processing. In addition, following a value reversal the feedback related negativity(FRN), an ERP component that reflects feedback processing, was amplified and co-occurred with increased N2pc amplitudes in trials following low-value feedback. Importantly, participants that showed the greatest adjustment in N2pc amplitudes based on feedback were also the most efficient learners. These results allow further insight into how changes in attentional prioritization in an uncertain and volatile environment support flexible adjustments of behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".