MétaCan
Menu
Back to cohort
Record W4401175579 · doi:10.1163/15507076-bja10026

Digital Storytelling to Amplify Heritage Learner Identities and Voices

2024· article· en· W4401175579 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHeritage Language Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDigital storytellingStorytellingIdentity (music)MultimediaSociologyComputer scienceArtNarrativeAestheticsLiterature

Abstract

fetched live from OpenAlex

Abstract This study presents four case studies of heritage learners of Spanish who participated in a digital storytelling project in an advanced Spanish course at a university in Western Canada. Through a series of face-to-face and remote workshops, each learner scripted a story containing a language-related event that allowed them to make meaning and reflect on their past experiences as related to language attitudes and ideologies experienced by themselves and others during the event. Data was collected from multiple sources including the participants’ videos and video scripts, written reflections, a questionnaire, and interviews. The participants’ stances and positionings within their digital stories, interviews, and reflections on the project offer revealing insights into their meaning-making processes through digital storytelling. Using a narrative analytical approach, the data analysis resulted in four overarching themes of personal growth, heritage speaker identity, positioning by others, and linguistic [in]security.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.031
GPT teacher head0.367
Teacher spread0.336 · 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