Bibliographic record
Abstract
Teaching Middle School Language Arts is the first book on teaching middle school language arts for multiple intelligences and related 21st century literacies in technologically and ethnically diverse communities. More than 670,000 middle school teachers (grades six through eight) are responsible for educating nearly 13 million students in public and private schools. Thousands more teachers join these ranks annually, especially in the South and West, where ethnic populations are ballooning. Teachers and administrators seek practical, time-efficient ways of teaching language arts to 21st century adolescents in increasingly multicultural, technologically diverse, socially networked communities. They seek sound understanding, practical advice, and proven strategies for connecting diverse literature to 21st century societies while meeting state and professional standards. Teaching Middle School Language Arts provides strategies and resources that work. Roseboro's book provides an entire academic year of inspiring theory and instruction in multimedia reading, writing, and speaking for the 21st century literacies that are increasingly required in the United States and Canada. An appendix includes supplementary documents to adapt or adopt, and a companion web site is designed to continue communication with readers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.024 |
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".