Bibliographic record
Abstract
The field of literacy is in the midst of a fastmoving evolution that challenges middle grade teachers to learn more about the nature of print and nonprint materials that students need and use, and about the approaches to literacy instruction that are required to keep pace with these changes. This themed issue on literacy topics offers three new perspectives on literacy instruction and assessment that enrich teachers and teacher educators’ understanding of the elements of literacy in all its forms: reading, writing, listening, talking, viewing, and representing visually.Amy Hutchison and Laurie Henry investigate the use of technology as they studied young adolescents. Their analysis of Internet use and online literacy skills among seventh graders reveals the in-and out-of-school literacy competencies of these students in terms of the digital divide that has been discussed by researchers. They offer interesting educational implications.Shelley Stagg Peterson, Jill McClay, and Kristin Main target in their article the art of teaching writing from the perspectives of Canadian teachers working with young adolescents. Their large-scale study using conversational interviews revealed the contemporary practices of middle grade teachers and provide a view into the changes in format and function of writing in schools today.Donna Werderich focuses on the literacy needs of young adolescent boys and the roles middle grade teachers may take as they dialogue with students through coaching and writing. Her examples of their discourse and journal entries as they navigate between reading and writing provide a model for mediating learning through conferencing events.Each of these articles provides insight into the evolving nature of literacy in middle school classrooms and the need for teachers to scaffold instruction and assessment to the needs of a new generation of young adolescents. The importance of reflecting on new approaches to literacy development cannot be understated as it mediates the learning in all content areas of instruction. We hope you find these articles informative for your practice and professional development. We thank the reviewers for their careful consideration and supportive feedback of manuscripts as we prepared this issue. We also acknowledge the help of Jennifer Schafran and Thomas Grant from Pace University for their time and invaluable assistance with management of the daily business of this issue’s compilation of articles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".