Outsiders looking in? Challenging reading through creative practice
Bibliographic record
Abstract
Becoming well read, especially in academia, is key to being part of the university - and of society. Academic reading is ‘tricky business’ especially for those widening participation students not necessarily familiar with the forms and processes of Higher Education. To foster these students’ academic literacies and practice, rather than the decontextualised teaching of ‘skills’, we create empowering social spaces for authentic collaborative reading. To facilitate this, we present text ‘differently’: text as scroll. A textscroll can be made by taping article or chapter pages together, side-by-side. Textscrolls open up the contested bookspace and make the written word accessible. They foster dialogic and multimodal interaction with texts and, if woven into a developmental embodied sequence of learning activities, help develop an understanding of academic reading as a wider social practice. Student and staff feedback show that scrolls are liberating. Scrolls, in embodied ways, make university reading meaningful and can authentically scaffold entry into epistemic communities. Scrolls help learners access the written word - and enjoy reading.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".