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Record W2906111834 · doi:10.1080/15313220.2018.1561638

Uncertainty, story-telling and transformative learning: An instructor’s experience of TEFI’s Walking Workshop in Nepal

2018· article· en· W2906111834 on OpenAlexaff
Joan Flaherty, Jonathon Day, Alison Crerar

Bibliographic record

VenueJournal of Teaching in Travel & Tourism · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTransformative learningPedagogyPsychologySociology

Abstract

fetched live from OpenAlex

This article recounts three stories from TEFI’s walking workshop in Nepal: the construction of a road through what was once a trekking path; a dance-floor encounter at a Himalayan party; and the arrival of one participant, fatigued by jet lag and disoriented by the new surroundings. These stories of confusion, discomfort and fear are linked by one common theme: the potential of uncertainty to foster deep reflection and nuanced conclusions. The premise that uncertainty is to be valued and even cultivated has been explored in educational theory, spiritual traditions, and research on transformative learning. These sources affirm the role of uncertainty in the process of knowledge creation. However, accepting this role can be challenging for educators because it requires they assume a new identity, one which they may perceive as being at odds with their status as “teacher” the identity of learner. One way for the educator to address this challenge may be through recounting their own stories of uncertainty. Stories of being immersed in unfamiliar situations that challenge, confuse and even frighten – stories, in other words of being a tourist – can foster reflection on an intellectual, emotional and spiritual level, engaging the “whole” person, and thus initiating the educator/learner’s transformative journey.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.095
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.344
Teacher spread0.322 · 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 teacher head, 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

Citations4
Published2018
Admission routes1
Has abstractyes

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