Values and Imagination in Teaching: With a Special Focus on Social Studies
Bibliographic record
Abstract
Both local and global issues are typically dealt with in the Social Studies curriculum, or in curriculum areas with other names but similar intents. In the literature about Social Studies the imagination has played little role, and consequently it hardly appears in texts designed to help teachers plan and implement Social Studies lessons. What is true of Social Studies is also largely reflected in general texts concerning planning teaching. Clearly many theorists and practitioners are concerned to engage students’ imaginations in learning, even though they use terms other than ‘imagination’ in doing so. This article suggests that a more explicit attention to imagination can make our efforts to engage students in learning more effective. We provide, first, a working definition of imagination, then show how students’ imaginations can be characterized in terms of the ‘cognitive toolkits’ they bring to learning. We look at such ‘cognitive tools’ as stories, images, humor, binary oppositions, a sense of mystery and how these can be used to engage students’ imaginations in learning Social Studies and other content from kindergarten to about grade four. We then consider ‘cognitive tools’ commonly deployed by students from about grade four to grade nine, including a sense of reality, the extremes of experience and limits of reality, and associating with the heroic. We also provide examples of how using such tools could influence planning and teaching Social Studies topics.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".