Sparking the historical imagination: Strategies for teaching Conceptual and Historical Issues in Psychology
Bibliographic record
Abstract
Roger Smith (2000) has written, ‘The historical imagination, surely, connotes many things… High on my list is the imagination to believe that our lives, experience, and knowledge… do not circumscribe what it is possible to live, experience, or know.’ In the spirit of cultivating our students’ ‘historical imagination,’ we introduce a number of activities and assignments that can be used both within the history of psychology course and across the psychology curriculum. We focus specifically on the novel ways disciplinary knowledge can be contextualised to equip students to question the universality and essentialism with which they are typically confronted in their psychology training. These strategies are a means of rendering psychology, past and present, both more personally meaningful and socially relevant to students. The examples provided are based on our experiences developing and implementing the history of psychology course at two large, public, urban universities in Canada over the past 10 years. They are informed by this multicultural milieu, as well as by our own orientations as feminist/gender historians. We include examples of imaginative classroom activities (‘The Case of Country X’ and ‘Speed Dating with a Psychologist’) and course assignments (‘Internationalising Psychology,’ ‘Imagined Conversations’, and ‘Graphic Novels’). In as much as we describe specific ways to engage students’ historical imagination, we also seek to spark the imagination of instructors to create their own exercises inspired by these examples.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".