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Record W6969330083 · doi:10.5683/sp3/jyt6qb

The Power of Stories: Examining the Impact of Storytelling on Learners

2023· dataset· en· W6969330083 on OpenAlexaff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStorytellingTreasureIndigenousThematic analysisPower (physics)Indigenous educationTable (database)Story tellingKey (lock)

Abstract

fetched live from OpenAlex

Indigenous story-telling is vital for the transmission of oral histories from generation to generation. In a local First Nation, storytelling comes in the form of creation stories and individual stories. A Land-based Healing Cultural Support Worker and community partner has been working closely with a UVIC Environmental Sciences student on the translation, preservation, and revitalization of her individual story, “My Search for My Way of Being” for many years, detailing both positive and negative human experiences and the treasure box of teachings that have come from her life. This project created a thematic analysis and framework for the story to be used as a tool for the community partner to teach to a broad range of audiences and environments. This project used a Two-Eyed Seeing approach to analyze a recorded sample of the community partner’s teachings, including translations of key words in her language. Four main themes were synthesized and organized into a table with relevant info for teaching and discussion questions: 1) Family and Ancestral Connections, 2) Language, 3) Intergenerational Trauma, and 4) Spirituality.

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)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.008

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 head0.025
GPT teacher head0.279
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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