Affinity Spaces and Ecologies of Practice: Digital Composing Processes of Pre-service English Teachers
Bibliographic record
Abstract
English educators are responsible for preparing pre-service and in-service teachers to consider the ways in which people engage in meaning making by using a variety of representation, interpretive and communication systems. Today new technologies are radically changing the types of texts people create and interpret even as they are influencing the social, political and cultural contexts in which texts are shared. This research project was designed to immerse pre-service English education students in the creation of multimodal, multimedia texts as part of a digital composing workshop. For the purposes of this paper, three student experiences were drawn from a group of twelve pre-service English education students participating in the project. Each student represents a unique experience from which we may draw insight and direction as English educators. Despite the ever present barriers to integrating afterschool (Prensky, 2010) literacy practices into traditional schools and to ensure what we are teaching has the important element of “life validity” ( Mills, 2010) and reflects the evolving socio cultural literacy practices of contemporary society, English educators must provide authentic, engaging opportunities for pre-service teachers to learn about and through multimedia, multimodal digital technologies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".