Canadian Social Studies 44(1) Cutrara
Bibliographic record
Abstract
Until the grand narrative is recognized as a product and process of power and privilege it can never do the transformational work that it can, and should, do in education. In this paper, I argue that concept learning can be a practical strategy for exploring and deconstructing power that is structured through the grand narrative and manifested in the nation. In particular, I introduce my pedagogical model of Historic Space as a transformative tool for having these discussions and support this approach by discussing students ’ interactions with it. Grand Narrative as a System Every good historian knows that history is not the past but merely a tool for making meaning from past events. By arbitrarily designating significance and highlighting the progression one significant person or event has from another, the past becomes a structure we call history. Although all types of history structure the past, grand national narratives are a particular type of structure that “explains the culture to itself and expresses its overriding purpose ” (Francis, 1998, p. 1). As a result, it is important to take the power of the grand narrative seriously and understand how its components work to establish and naturalize meaning in our contemporary world. Despite the best pedagogical intentions, shallow grand narratives filled with names and dates pulled from the past, deemed important for (re)telling, and situated within a mythical narrative structure, remain the most salient type of history learnt in schools. In these grand narratives, “the actual histories that people live, their complex interactions with others, are obscured and eventually forgotten ” (Stanley, 2003, p. 38). People in history become presented as “simplistic, one-dimensional, and truncated portraits ” of themselves (Alridge, 2006, p. 663) and events in history become reduced to “their essential traits, their final meaning or their initial and final
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".