Review of Angela Laflen’s Critical Data Storytelling in the Composition Classroom
Bibliographic record
Abstract
Increasingly, we understand and navigate the world via dataCied ways of knowing.Online activities are transformed into big data, which is presented back to us in stories and arguments.In this process, data shapes our thinking.This is the starting point for Angela LaClen (2025) in her book Critical Data Storytelling in the Composition Classroom (Utah State University Press).Through our online activities, we are building a stock of "previously unimaginable quantities of data" (p.187) that is tracked and analyzed, that is used to tell stories and build arguments, and that stokes AI.LaClen contends that the use of data, especially numerical data presented in visual formats, is a powerful form of multimodality and that the development of critical data literacy skills should be a priority for instructors in multimodal writing classes.In Critical Data Storytelling, LaClen's concern, then, is not whether professors should allow students to use generative AI in the writing process (a frequent focus of discussion) but on the development of students' critical data literacy skills, given the ubiquity of data and dataCied arguments.Her central point is that writing instructors should prioritize critical data literacy, creating a bridge between academic skills and the everyday skills students need to move critically through dataCied environments.To support instructors who are beginning the journey, she provides sample writing assignments throughout the book and a chapter focused on assessment.For me (a writing instructor grounded in traditional academic literacies), LaClen's book provides a good stretch, showing how to centre the data literacy skills that instructors and students urgently need in a world permeated with mis-and disinformation.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".