Bibliographic record
Abstract
The authors have come to believe, based on several years of collaborative work in an alternative literacy program, that developing the multiple literacies of struggling youth requires a curricular playfulness with students' ideas, biographies, and imaginations across genres and media. Social literacy and multiliteracy theories are powerful and important reminders that literacy practices are varied and situated across different media and that school‐based literacy practices need to be inclusive of a broad range of students, cultures, and text formats. The youth literacy program described in this article leans on this work and recent reconceptualizations of adolescent literacy by integrating verbal and visual imagination, the material contexts and biographies of student lives, traditional print‐based literacies, and opportunities for students to express themselves across multiple literacies. The authors build on language and nonlanguage skills, which may or may not be valued in many schools, and scaffold literacy skills needed to augment student life skills and talents. To bring these arguments to life, the authors provide examples of the ways students rearticulate, transform, and integrate their own narratives into curricular texts through poetry, art, music, and video—activities that can, at times, lessen or mitigate their resistances to schooling.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".