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Record W2087595973 · doi:10.2304/elea.2007.4.3.273

Adolescents Composing Fiction in Digital Game and Written Formats: Tacit, Explicit and Metacognitive Strategies

2007· article· en· W2087595973 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueE-Learning and Digital Media · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsNarrativeAffordanceGame studiesMathematics educationDigital literacyMultimediaComputer sciencePedagogyPsychologyHuman–computer interactionLiteratureArt

Abstract

fetched live from OpenAlex

This article reports on a study of 23 tenth-grade students who created fiction in digital game and written formats. The researchers observed them at work, analysed their stories in both formats, and interviewed selected students to learn what affordances and constraints they demonstrate and/or articulate in such authoring. The students used ScriptEase, a software tool that supports the creation of digital stories, based on the game engine of Neverwinter Nights (Bioware). The authors consider the theoretical literature about narrative and games, focusing especially on indicators of verbal tense and mood. They discuss the overlaps and differences between digital and written stories, drawing in particular on the work of two students, and they conclude with implications for theoretical understandings of contemporary narratives in multiple formats and implications for literacy education.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.272
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it