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Record W2917088323 · doi:10.4236/ce.2019.102026

Engaging Indigenous Youth in Science with the High-Altitude Balloon Experiment

2019· article· en· W2917088323 on OpenAlexafffundabout
Stephen C. Cheng, Fidji Gendron, Vincent E. Ziffle, David Gerhard

Bibliographic record

VenueCreative Education · 2019
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsFirst Nations University of CanadaUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsIndigenousEffects of high altitude on humansCurriculumAltitude (triangle)BalloonSet (abstract data type)Event (particle physics)Door-to-balloonMathematics educationPsychologyMeteorologyGeographyComputer sciencePhysicsPedagogyMedicineMathematicsEcologyBiologyCardiology

Abstract

fetched live from OpenAlex

Our custom high-altitude balloon experiment kit with the complete set of instructions has been successfully used to engage high school and post-secondary students across Canada. This article describes how the high-altitude balloon experiment was adapted to engage Indigenous students from two on-reserve schools in science with the presence of an Elder. Based on the results from our research, while the balloon experiment is an effective tool to engage Indigenous students, the project doesn’t change the participants’ interest in science. We are making several suggestions to bring the experiment to its full potential. It would be more beneficial to make the high-altitude balloon experiment as a multi-day workshop or a major component of a science summer camp. Further, it would be more effective to integrate the balloon experiment into the high school science curriculum rather than run it as an independent event in the on-reserve schools. Finally, we are suggesting how student participation of the survey can be improved for on-reserve schools.

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)0.003
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 score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.331
Teacher spread0.318 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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