Related but distinct: An investigative path amongst the entwined relationships of ideology, imaginary, and myth
Bibliographic record
Abstract
Many educational studies reference ideology, imaginary, and myth constructs represented in programs of study, textbooks, and school rituals. In the fields of history, civic, and social studies education, for example, many scholars frequently employ these terms to examine mythic groundings of particular nationalisms entwined with the ways in which we perceive history and citizenship education. However, the lack of philosophical clarity about these concepts raises some crucial questions: in what ways should we distinguish these often overlapping key terms? How might they be put into relation for the purposes of researching such and engaging these terms pedagogically? To respond to these questions, I seek to outline the characteristics of and entwined relationships among ideology, subject, imaginary, and myth by engaging key influential scholarly and historical works. With the considerations of these characteristics and relationship, I attempt to add more precision to the conceptual bases crucial to elucidate unequal relations of power in curricula and schooling practices addressed across many fields constituting educational studies. In doing so, I hope that this groundwork provides curriculum scholars and teachers with meaningful ways to deliberate and employ these key concepts in the contexts of educational research and everyday schooling practices.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.106 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".