From Landfill to Loom: Two Teacher-Researchers Chronicle Their Sustainability Narratives via The Secret Under My Skin
Bibliographic record
Abstract
Traditionally, the responsibility for sustainability education has been assigned to the fields of science, engineering, technology and outdoor education. More recently, English language arts have begun to play an integral role in educating students on the importance of preserving the environment for future generations. Pertinent research, however, indicates that many teachers, including those teaching English, do not feel fully prepared to address sustainability in their classrooms. Such teachers would benefit from either pre-service or in-service support where they would have opportunities to gain more knowledge about sustainability, while also critically inquiring into their related pedagogical beliefs and practices. Before beginning the process of planning and implementing relevant sustainability education experiences for English teachers, it is imperative that we, two teacher educators, first examine our own teaching narratives related to this important topic. Focusing on the dystopian young adult novel, The Secret Under My Skin (McNaughton, 2000), we make generous use of Rosenblatt’s (1995) transactional theory of reader response to critically inquire into past experiences that shape our recurrent views and actions in the classroom. We express our back-and-forth transactions in interspersing sections of poetry, prose and image, including emerging questions to consider as starting points for future engagement with teachers on the integration of sustainability education into the English language arts curriculum at the secondary school level.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.013 |
| 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".