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Floating narratives: transnational families and digital storytelling

2018· book-chapter· en· W2913791636 on OpenAlexaboutno aff
Catalina Arango Patiño

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeDigital storytellingStorytellingComputer scienceArtMultimediaLiterature

Abstract

fetched live from OpenAlex

This chapter examines the effects of information and communication technologies (ICTs) on storytelling as a practice of communication among transnational families. It describes three technological affordances that are linked to digital storytelling practices of six Colombian migrant families residing in Montreal, Canada: presence, interactivity, and multimodality. After providing an overview of the methodological approach employed in the research study and the techniques used to collect and analyse the data, the chapter discusses the findings with regard to the views of the participant families about the dynamics of their post-migration storytelling experiences. More specifically, it considers the Colombian families' perspectives about being present during their digital interactions. An important finding is that digital mediation seems to be altering family storytelling. For some families, ICTs catalyse storytelling in situations where presence and multimodality take place; for others, ICTs constrain family storytelling when the illusion of nonmediation is not experienced.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.009
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.094
GPT teacher head0.372
Teacher spread0.277 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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