Youth Conceptualizations of Evil and Implications for Social Studies Education
Bibliographic record
Abstract
It is crucial that social studies research attempt to understand students' conceptualizations of evil because a longstanding societal issue is politicians furthering their agenda through the exploitation of the semantic impact of "evil" (Dews, 2008).If social studies teachers are informed about youth conceptualizations of evil, they might approach curriculum in a more meaningful and critical way, particularly contemporary events.George W. Bush infamously used the phrase "axis of evil" as a rallying cry for the United States' war in Iraq (Bush, 2002) and more recently Stephen Harper has dubbed Iran as evil and also linked Nazism, Marxist-Leninism, and terrorism together as reinventions of a similar evil that seeks to destroy "human liberty" (Marsden, 2012;Perkel, 2014).Evil is a familiar social signifier in politics and popular culture, but it is rarely defined or discussed.Yet, students' nascent understanding of evil informs how they interpret historical and current events and whether they see such events as inevitable, thus affecting their sense of future possibilities.Dissecting the word itself and the concept of evil can change how teachers approach historical atrocities (e.g., genocides like the Holocaust/Shoah) and current events (e.g., political posturing over Iran and Russia).A significant lack of scholarship exists regarding how youths conceptualize evil.To begin to address this gap, we are reporting preliminary research that has shown a complexity in youth understandings of evil, highlighting the need for more exploratory research.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".