Allusions, Illusions and Learning: Integrating story into the broader curriculum
Bibliographic record
Abstract
The workshop will be a visual presentation covering the rationale for a unit of research that involves integrated learning/curriculum and concentrates on critical literacy. The presentation will also involve a complete example unit and accompanying course booklet. Participants will be involved in a handson evaluation of the unit presented and assess its suitability to adapt to other “core literature genres”. The unit of work involves the “multiple faces of literacy: Reading. Knowing. Doing.” at the practical teaching level. The aim is to show how to use texts from “classic fiction” in research units, as vehicles for learning, not only about the texts themselves, but also about their wider cultural significance and associated literary forms, as well as these stories represented in other media. The unit attempts to place everything examined in a meaningful context and to provide opportunities for students with diverse learning styles to enjoy the unit and to succeed. The objective is to make classic stories the integrating vehicles for learning, and so change them from literary “illusions” to “allusions” once more, therefore showing that the “critical literacy” approach can use traditional literature or story in teaching.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".