Contrapuntal writing: Student discourse in an online literature class
Bibliographic record
Abstract
ABSTRACT: There is a continuing need to investigate how contemporary students in schools are writing the word, and their world, beyond modernist parameters of the page. This article explores the online writing of a senior English world literature class, located in a Western Canadian city, as examined through a recent qualitative case study. Borrowing a 17 th century musical term meaning “of counterpoint”, contrapuntal here is used to describe the visibly polyphonic and layered writing by students and their teacher in the online course. Complex constructions and understandings of situated self/culture in relation, or as counterpoint, to other members of the class, their teacher, and their various prescribed/personal texts were made throughout the course. Discordances were voiced, but also played out as silences. The class’s emergent and evolving writing provides a grounded glimpse into critical literacy practices. Particularly evident was a developing meta-cognition throughout the students ’ writing – their ability to “read ” the other writers so that they could be reflexive about their own practices. Yet, little was done by the students to critique or transform the constructs of the course itself. This seemingly contradictory aspect further manifests an equally important, yet for critical literacy theorists, frustrating characteristic of contrapuntality – that the various composers/voices follow strict structural rules.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".