Holding water: An index of affectual possibilities and “shuttling intensities” across the social studies
Bibliographic record
Abstract
This article theorizes how affect—conceptualized as an embodied intensity of feeling distinct from emotion—can open up alternative pathways of inquiry in social studies classrooms. After discussing prior connections between social studies education research and affect, emotion, and feeling, we focus our analysis on media texts covering two historically significant events: the boat evacuation of Manhattan on September 11, 2001 and the U.S. Capitol riots on January 6, 2021. Through our analysis, we aim to demonstrate how affective readings of diverse texts can provide social studies teachers and students with lines of inquiry (and flight) that could complement more prevalent foci on historical thinking. Through an affective framing, we argue that using media to engage with representations of historical encounters creates multiplicitous pathways that veer away from solely teaching and learning about history to teaching and learning about how history is felt.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".