The Emotional Climate Scale: Understanding Emotions, Context and Justice
Bibliographic record
Abstract
The Emotional Climate Scale (ECS) was used to study the emotional responses of minority and majority elementary school students to different settings within their schools. The ECS applies a 7 point Likert scale to assess the emotions of anger, sadness, anxiety, loneliness, calmness, excitement, happiness, and hope in the school settings of the school bus, the playground, the principal office as well as the English, mathematics, social studies, and science classes. Minority children are significantly happier and lonelier on the bus and they are also more excited, angry and lonely in English classes than their white peers. In math classes minority children are more excited but lonelier. On the playground minority children are significantly sadder than majority children. In the principal’s office, minority children are significantly calmer than majority children. In science, minority children were significantly more excited and hopeful. In social studies minority children were also more excited. In the gym, there were no significant differences between majority and minority children. Excitement and happiness are the two positive emotions are preferred and appropriate for all classes. A key question raised by the ECS is the question “Is this school fair to minority children?” The presence of loneliness, sadness, and anger are troubling indicators of something that is not right in this school.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".