Bibliographic record
Abstract
As a curriculum researcher and poet, I am committed to honouring complexity, asking questions, acknowledging tensions, and challenging answers. As curriculum theorists, scholars, and educators, we need to attend to language. Ted T. Aoki (2005a) reminds us to attend to “the voice of play in the midst of things—a playful singing in the midst of life” (p. 282). Poetry can invigorate our curriculum studies by helping us imagine new ways of attending to language, new ways of knowing and becoming, and new ways of inquiring about living experiences. In this paper, I offer a sequence of poems, anecdotes, and ruminations composed as responses to Ted T. Aoki’s curriculum scholarship. En tant que poète et chercheur penché sur les programmes d’études, je suis engagé à respecter la complexité, à poser des questions, à reconnaitre des tensions et à remettre en question les réponses. Comme théoriciens du curriculum, universitaires et éducateurs, nous devons porter attention à la langue. Ted T. Aoki (2005a) nous rappelle de porter attention à « la voix ludique au milieu des choses – un chant joueur au milieu de la vie » (p. 282). La poésie peut dynamiser nos études de curriculum en nous aidant à imaginer de nouvelles façons de concevoir la langue, de nouvelles façons de savoir et de devenir, et de nouvelles façons d’étudier le vécu. Dans cet article, j’offre une série de poèmes, d’anecdotes et de ruminations composés en guise de réponses aux recherches de Ted T. Aoki sur les programmes d’études.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.003 |
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; both teacher heads agree on what is shown here.
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".