Bringing Curriculum Down to Earth: The Terroir That We Are
Bibliographic record
Abstract
In this essay, the authors outline theoretical and practical considerations that arise out of their autobiographical curricular research into place, identity, and food. Regarding the quintessential curricular questions, What is worth knowing? and How do we come to know?, they posit that an attention to how we eat and use the world (Berry, 1990) is a curricular endeavor. As such, they understand their work here as an acknowledgement of embodied or somatic and visceral, sensual knowing, a celebration of and attunement to the everyday experiences in human lives. The notion of terroir is explored as one possible approach to "bringing curriculum down to earth" and to dwell in the humus (Aoki, 1991/2005) we share with other living things. Along with a series of six vignettes that illustrate how food and place have influenced their identities and vice versa, the authors offer the reader, by way of sidebars, some of the strategies that have transformed their praxis when working towards an educational philosophy and curriculum that honours terroir not only as a theoretical or conceptual idea/ideal but also as a viable and sensual embodied practice. In this format, the essay mixes abstract theoretical discussion with examples of concrete praxis. The authors suggest that this mixing is an important and necessary curricular endeavour for
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".