Unveiling the stories that illuminate our path: The pedagogical significance of autobiographical study and the method of currere
Bibliographic record
Abstract
In today’s educational landscape, instrumentalist ideologies embedded in politically entrenched school curricula often overshadow the richness of diverse human experiences, perpetuating colonial shadows within educational experiences. In this paper, through the sharing of our juxtaposed autobiographical stories, we intend to exhibit the pedagogical significance of autobiographical inquiry and the method of currere as empowering individuals to transcend the limitations of an arrested self – a persona moulded by a factory-like schooling system that merely serves instrumental ends. We seek to address the question: How might the process of autobiographical study and the method of currere impact pedagogical praxis attuning it to individual lived experiences? By examining the specificities of each event in an individual's life and reflecting on the interplay between personal experiences and education, teachers and students can better comprehend their world through the lens of their lived experiences. Therefore, this paper underscores the pedagogical importance of autobiographical study and the method of currere encouraging educators to attune with an educational praxis anchored in personal experiences. Furthermore, it introduces the transformative potential of these methods to reimagine the different possibilities of praxis in education.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".