Bibliographic record
Abstract
This article offers a suggestion for a visual representation of writing assessment and its related research enterprise. In today’s world of visual data and multiliteracies, representation through images is recognized as a powerful tool of inquiry that can transform our understandings and our research practices (Sanders-Bustle, 2003). Although previous visual representations in the eld of language assessment have been useful, they have been predicated on two assumptions: that all writing assessment consists of formal tests, and that assessment processes are linear rather than recursive. This new proposed visual representation has potential to aid in reconceptualizing the interrelated elements of writing assessment, as well as revealing new relationships among elements to explore. Such awareness can bene t anyone involved in the writing assessment enterprise, from writing assessment scholars to classroom teachers of writing. Cet article offre une suggestion de représentation visuelle de l’évaluation de l’écrit et de la recherche qui en découle. Dans notre monde de données visuelles et de li ératies multiples, la représentation par les images est reconnue comme un outil d’enquête puissant qui peut transformer notre connaissance et nos pratiques de recherche (Sanders-Bustle, 2003). Si les représentations visuelles antérieures dans le domaine de l’évaluation des compétences linguistiques ont été utiles, elles ont aussi été basées sur deux hypothèses : toute évaluation de l’écrit consiste en des épreuves formelles et les processus d’évaluation sont linéaires plutôt que récursifs. Ce e nouvelle représentation visuelle a le potentiel d’appuyer la reconceptuali- sation des éléments interreliés de l’évaluation de l’écrit et d’exposer de nouveaux rapports entre les composantes de l’écriture. Toutes les personnes impliquées dans l’évaluation de l’écrit pourraient en pro ter, qu’elles soient des professeurs en évaluation ou des enseignants de l’écriture.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".