Envisioning possibilities: visualising as enquiry in literacy studies
Bibliographic record
Abstract
Abstract Drawing from the research methods of three distinct literacy studies, in this piece, we highlight the visualisation approaches integral to our enquiry processes as researchers working to make sense of literacy and learning. We aim to encourage, provoke even, a conversation about visualisation processes in literacy research by sharing the individualised ways in which we (1) leaned on metaphors and visual aspects of musical notation to uncover new insights into the social nature of composing, (2) created comics shaped by particular aesthetic choices that influenced enquiry and meaning‐making, and (3) utilised insights gained from dynamic visualisations of data to see nonlinear patterns of writing development. With the descriptions of these studies and their methods as samples, we argue for a shift in focus from visualisation as an end product of analysis to an additional focus on the process of visualising as analysis.
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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 | 0.036 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".