MétaCan
Menu
Back to cohort
Record W3036975346 · doi:10.5206/eei.v30i1.10915

Showing the Way to Inclusive Outdoor Education: Impact of Hands-On Training in Adapting a Kayak

2020· article· en· W3036975346 on OpenAlexaffvenue
Jessica Delorey, Erin L. Austen, Andrew Foran

Bibliographic record

VenueExceptionality Education International · 2020
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsTraining (meteorology)Inclusion (mineral)Test (biology)PsychologyMedical educationTransfer of trainingService (business)Control (management)Outdoor educationMathematics educationPedagogyComputer scienceMedicineSocial psychologyBusinessMarketingCognitive psychology

Abstract

fetched live from OpenAlex

Insufficient training in using adaptations and specialized equipment for outdoor education practices is a barrier to inclusion in public schools. Providing teachers with hands-on training opportunities in adaptations could be beneficial. Two training groups, one of pre-service teachers (n=19) and one of inservice teachers (n=18), were given direct exposure to adapting a kayak to make it accessible to users of different abilities. Participants had the opportunity to discuss the kayak adaptations and to interact with the equipment. Pre-service teachers who did not yet have formal outdoor education instruction (n=18) served as a control group in this pre-test, post-test design. Training increased participants’ self-efficacy and their willingness to adapt kayaks in the future. These positive effects did not, however, transfer directly to other activities, nor did the training impact overall inclusion attitudes. Nonetheless, direct exposure to adaptations is a promising training tool for demonstrating to teachers that implementing inclusive outdoor education practices is doable.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.078
GPT teacher head0.422
Teacher spread0.344 · 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 designObservational
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

Citations4
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueExceptionality Education InternationalSame topicRecreation, Leisure, Wilderness ManagementFrench-language works237,207