Bibliographic record
Abstract
This presentation explores the interplay between the researcher’s understandings of multimodal literacies (Walsh, 2011) and the design of a digital, multimodal, research journal using the Google Keep application (Google, 2018). The examples come from a year-long action research study (Pine, 2009) designed to inform the pedagogies of a novice educator. The research journal design was built upon understandings of multimodal literacy that recognize that meanings can be shaped through the researcher-digital interface (Kuby & Rowsell, 2017), and it was extended from uses of Google Keep for pedagogical documentation in kindergarten classrooms (Vaillancourt, 2017). The presentation highlights the ways that the researcher created the journal using text and publicly useable images and analyzed the data through hashtag labels. Through this discussion, the researcher considers the ways meaning was shaped through the affordances and constraints of the journal. The presentation contributes to knowledge related to the ways that multimodal literacy, and digital tools designed for teaching practice, can be used within research designs.
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 | 0.038 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".