A Review of “Next level grammar for a digital age: Teaching with social media and online tools for rhetorical understanding and critical creation”
Bibliographic record
Abstract
Next level grammar for a digital age by Darren Crovitz, Michelle D. Devereaux, and Clarice M. Morgan has made a valuable contribution to English Language Arts (ELA) education by delineating how grammar instruction can be situated within real-life contexts using digital technologies and media, as well as emphasizing the importance of critical digital literacy in ELA education. The book provides educators with a wide range of approaches to guide students in utilizing language in digital spaces and understanding rhetorical grammar to create digital content, aiming to raise learners to become more conscious digital readers and writers and to grow up to be engaged citizens. With its concrete and practical lesson ideas, the book helps language teachers to prepare for teaching complex and challenging topics that emerge in digital realms and to guide students to incrementally develop their literacy skills. Individuals in language education will find valuable insights for engaging discussions on how language teaching can be updated to meet requirements of an increasingly digital media landscape.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".