Living as Textual Animals: Curriculum, Sustainability and the Inherency of Language
Bibliographic record
Abstract
The ecosystems that sustain us are sending warning signals we ignore at our peril. Environmental and economic indicators tell us how we live in our respective places must change radically. Education in general and curriculum developers specifically, struggle to find an appropriate response to an impending crisis. Literacy learning focuses on the spoken and written word as well as representation that contribute vitally to how individuals understand maintain and transform their worldview. The language arts classroom is potentially a powerful site for challenging taken for granted cultural assumptions. A research project undertaken with middle school students was designed to allow the students to address questions that increase awareness of how we live in our places. The project provided students the space to record connections, observations, descriptions and evidence of ecological relationships that emerge out of daily living. By attending to felt sense through an embodied approach to writing and response, students developed a deeper sensibility for the existence of their ecological selves. Students were able to sensitively address inner connectivities of body, mind and emotions to awaken and develop a deeper connection with the living landscapes in which they dwell demonstrating the integral role the literacy classroom will have in efforts to re-orient education to teach for the values of sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".