Deep Ecology and the Roots of Resilience: The Importance of Setting in Outdoor Experienced-based Programming for At-risk Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".