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Enregistrement W2945423938

Deep Ecology and the Roots of Resilience: The Importance of Setting in Outdoor Experienced-based Programming for At-risk Children

2018· article· en· W2945423938 sur OpenAlexaff
Michael Ungar

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Langueen
DomainePsychology
ThématiqueOutdoor and Experiential Education
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésRecreationPsychological resilienceWildernessResilience (materials science)EcologyPsychologyAdventurePublic relationsSociologySocial psychologyPolitical scienceComputer scienceArtificial intelligenceBiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Therapeutic, educational or recreational programming that promotes a deep and meaningful connection with nature is frequently used by social workers and allied professionals to mitigate risk and promote resilience in children. This paper addresses the bias found among those who support these programs which devalues the urban/developed environments in which most at-risk populations reside in favour of pristine/natural settings. Combining the philosophy of deep ecology and principles of social justice with findings from the study of risk and resilience, it will be shown that outdoor experience-based programming (OEP) contributes best to healthy outcomes in at-risk populations when programming provides participants an appreciation for the complexity and challenges they face living in the familiar environments they call home. The link between outdoor experience-based programming (OEP) and positive outcomes among participants whom are members of at-risk populations has been shown in a number of studies (Burns, 1999; Cooley, 1998), though results are methodologically problematic and met frequently with calls for further research (Davis-Berman & Berman, 1999; Ewert, McCormick & Voight, 2001; Neill & Heubeck, 1998). Despite this, there is enthusiastic support for these programs from professionals, communities and even children themselves (see Gillis & Ringer, 1999; Hirsch, 1999; McGowan, 1997). OEP includes a wide spectrum of activities that are offered to at-risk children from wilderness adventure and therapeutic recreation to environmental awareness, education and activism to promote social and environmental policy reform. Unfortunately, a review of the research on OEP shows that nature based programming does not sustain long-term change in individual participants and that the most positive outcomes result from the challenge of hands-on experience and constructive social interactions rather than immersion in nature, increased environmental awareness, or meaningful engagement in social action. Neither an appreciation for the intrinsic value of others and their communities, nor the growth of a sustained empathy for a biocentric (nature-centred) perspective has been shown to result directly from programming taking place out of doors. Nature as setting is often valued more by the facilitators of these programs than the participants (Witman, 1993). In this paper, I deconstruct this problem in an effort to provide a more theoretically sound argument for OEP that shows how programming can mitigate risk and produce more enduring socially just and environmentally sensitive outcomes. Drawing on the literature concerned with the philosophy of deep ecology and social justice and studies of risk and resilience I will show that the utilitarian view of nature underlying OEP reinforces an understanding of nature as “other” than that which participants experience as their environment. My goal is to challenges the way OEP emphasizes contact with pristine, unpopulated nature. The “natural” but populated environments in which at-risk children live (whether urban or rural) are made to seem different and dysfunctional when compared with the settings in which OEP takes place. The result has been further marginalization of the environment the child identifies as home. Furthermore, lessons learned from time spent in pristine nature cannot be transferred from this alien setting to the child’s own populated home context after programming is complete without the prolonged assistance of professional helpers. These helpers are needed to create continuity and integration of the lessons learned during OEP. Discussion of this role during and after programming is particularly germane to social workers. As a profession, we are frequently part of these programs as administrators and facilitators, and more than any other discipline have historically been concerned with a focus on the person-in-environment (Ungar, 2002a; Wakefield, 1996;). Specifically, my argument is two-fold. First, we require a more deeply ecological and socially just orientation to programming in order to address our commonality with nature which transcends the dichotomous thinking that emphasizes only those similarities found between humans and the natural pristine world. We need instead to construct a view of nature as being all around us, even in urban environments. Second, we know from years of study by social workers of the person-in-environment and the literature on risk and resilience that growth and adaptation must necessarily take place in one’s own environment (human and natural) to be effective. Lessons a child learns outside that which is familiar may be valuable, but are seldom adequate to cope with the immediate health challenges he or she faces day-to-day in the real world setting in which he or she lives.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,086
Tête enseignante GPT0,540
Écart entre enseignants0,454 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2018
Routes d'admission1
Résumé présentoui

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