The Mystery in Curriculum Development: Coming to Know Ourselves as Teachers and Individuals in the World
Bibliographic record
Abstract
In this paper, we address mystery in curriculum development in a personal and storied way. We share our belief in the ongoing nature of mystery across time and situation, in the teaching life and beyond, into the worlds we inhabit in our daily lives with others. As self-study narrative researchers over many years, we turn to the work of Michael Connelly and Jean Clandinin, William Pinar, Ted Aoki and Maxine Greene to illustrate our perspective that mystery awaits us as we uncover new meaning in the seminal stories we have lived, and which inform us in present day learning and understanding in an ongoing way. We describe two theories that we utilize to guide our path to unearthing the mystery held in our stories—one is Connelly and Clandinin’s narrative inquiry and the other, Pinar’s currere. Both consider curriculum development to be an ongoing and circular course wherein it is always possible to find new meaning from experiences that can open one to new, present-day worlds, where one can live and share interactions with others.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.079 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".