Neverwinter Nights in Alberta: Conceptions of Narrativity through Fantasy Role-Playing Games in a Graduate Classroom
Bibliographic record
Abstract
Computer role-playing games offer a unique opportunity to aid graduate-level analysis of hypermedia narratives. In this paper, we discuss the application of complex game authoring tools in HUCO-616: Multimedia in the Humanities, a graduate multimedia course in humanities computing (Huco) at the University of Alberta. Offered annually, this course is intended for students in the second year of their program. Much of the content for this article relates to experiences in the academic years 2003-05. The Huco program embraces a pedagogical imperative to marry theoretical and practical elements (Sinclair and Gouglas 2002; Gouglas, Sinclair, and Morrison, forthcoming). We believe that only those learners who have facility with a particular technology can effectively understand how that technology alters and informs interactions with source material. To paraphrase George Grant (1976), the computer does indeed impose on us the way it will be used; only through informed use can we understand this impact. For many students, the practical component of HUCO-616 was an unprecedented educational experience that required them to reflect on concepts of narrative and interactivity through active engagement with hypermedia environments and the creation of personal digital narratives (Exhibit 1). This approach contrasts common pedagogical practice in various social science and humanities programs where students are rarely asked to try their hands at creating the types of work they are being trained to study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".