(Re-) Mapping the System: Toward Dialogue-Driven Transformation in the Teaching and Assessment of Writing Authors
Bibliographic record
Abstract
Over three days, 180 junior and senior high school English teachers, postsecondary (university and college) writing instructors, workplace (corporate and small business) writing instructors, and government officials who are responsible for portfolios related to workforce training and literacy met to understand from a broad systems-level perspective how writing development was being supported and assessed in Alberta Canada. Conversations were structured using Dynamic Criteria Mapping (Broad, 2003) as a method for understanding the values, expectations, and contextual factors that shape the system. Participants shared values related to clarity of expression, risk-taking, and ability to motivate audience. These values, however, were enacted differently within school and workplace contexts. Writing as a problem-solving activity was identified as a tool for enhancing knowledge transfer within the system. Alberta’s large-scale writing exams, on the other hand, created barriers to transfer and development by undermining shared values within the system. Recommendations related to curriculum redesign, pedagogical change, assessment reform, and professional development are suggested for enhancing students’ longitudinal development as writers.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".