“Course” Work: Pinar's Currere as an Initiation into Curriculum Studies
Bibliographic record
Abstract
In this article, four new doctoral students reflect on Pinar’s currere process as an initiation into the discipline of curriculum studies. Currere involves examining one’s experiences as curricula that shape understandings: each of us undertook the steps of currere individually and then shared our reflections through collaborative autobiography. This collaboration expanded our self-reflexivity in relation to curriculum and to discursive contexts and, unexpectedly, created an authentic learning community. The currere process has not only written us into curriculum studies, but also compelled us to “participate in the constitution and transformation of ourselves” (Pinar, 1994, p. 74) that is so vital to our work in education. The following article—which consists of collaborative and personal writing—describes a valuable practice for bringing graduate students into curriculum studies. It also considers whether the self-reflexivity encouraged by currere might still be relevant for contemporary scholars and educators almost four decades after its inception.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.037 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".