Reading practices and digital experiences: An investigation into secondary students' reading practices and XML-markup experiences of fiction
Bibliographic record
Abstract
This article reports on a study of XML-markup experiences as reading practices of secondary students studying English literature in a public high school in Vancouver, British Columbia, Canada. Since training in the digital humanities (DH) has historically been restricted to those in undergraduate and graduate programs, an important consideration in DH education is how we might implement DH methods in secondary school curricula with a view to introducing prospective scholars to the field prior to their admission to post-secondary education. A concomitant goal would be to investigate a new locus for DH education, at a different level of education and in a different institutional environment, to observe how or if DH might migrate from the locale to which it has acclimatized. Our work maps a method for developing this pedagogy and presents findings on students’ semantic tagging of two short stories: Ernest Hemingway’s (1927) ‘Hills Like White Elephants’ and Sean O’Faolain’s (1948) ‘The Trout’. Analysis of this tagging reveals markup as reading practice and describes how students negotiate between the experiences of reading, how these experiences may be realized by text, and the ways in which XML markup—as process—mediates between.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".