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Record W4386420834 · doi:10.1080/14729679.2023.2254861

From McDonaldization to place-based experience: revitalizing outdoor education in Ireland

2023· article· en· W4386420834 on OpenAlexaff
John Pierce, John Telford

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2023
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsCamosun College
FundersMunster Technological University
KeywordsOutdoor educationRationalisationIrishSociologyPlace-based educationPedagogyConversationEnvironmental education

Abstract

fetched live from OpenAlex

Outdoor education in Ireland, as in many countries, takes place in a variety of physical locations ranging from urban to wilder, minimally human influenced environments. Irish public outdoor education providers have traditionally placed little emphasis on cultural understandings of the places where learning occurs. Moreover, outdoor education commonly demonstrates characteristics of a McDonaldized experience as opposed to a place-based experience. This paper explores two topics that may help to explain why place is not to the fore in teaching and learning in Irish outdoor education practice: historico-cultural relationships with the land, and the impact of the rationalisation of place on outdoor education. We approach this conversation from the belief that places, as well as people, can teach and that a more conscious pedagogical engagement with place encourages deeper, richer learning experiences. We conclude this paper by outlining how a more place-focused practice may be developed in (Irish) outdoor education.

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 score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.024
Scholarly communication0.0100.007
Open science0.0020.019
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.372
Teacher spread0.357 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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