Transformative learning through conservation : a case study of the Arabuko-Sokoke Schools and Eco-Tourism Scheme, Kenya
Bibliographic record
Abstract
Protected areas are seen as important tools for conserving biodiversity and species habitat, but the relationship between neighboring communities and these areas is often contentious, especially in Africa.It is being increasingly recognized that, if conservation is to be successful, conservation initiatives like protected areas should have the support of local residents.Studies have shown that support for conservation by residents is related to the level of benefit they derive from it and that this link is strongest when the benefits are more tangible.As such, there has been a concerted effort by conservationists to bring communities "on side," with a community conservation approach that attempts to involve residents in conservation in return for economic or other benefits.The ASSETS (Arabuko-Sokoke Schools and Eco-Tourism Scheme), operating in communities surrounding Kenya's Arabuko-Sokoke Forest, is one such conservation project.Kenya's Arabuko-Sokoke Forest is an area of intemational conservation concern: an important bird area, and. a stronghold of endemic species.However, residents surrounding the forest, among the poorest in the country, are facing a myriad of environmental and social challenges.Many residents have a negative view of the forest, often a result of the crop damage they endure from forest animals, and past studies have indicated that many residents would like the forest cleared for agriculture.ASSETS, a conservation program initiated in 2001, attempts to reduce dependence on forest resources and foster a more positive attitude towards conservation by channeling eco- tourism profits from the forest to community members in the form of secondary school bursaries.Using a qualitative, case-study approach, this project assesses the impact of ASSETS in Kaembeni, Kilifi District, focusing on participant learning and the extent to iii I which such learning results in a more positive attitude towards forest conservation and the adoption of less destructive resource uses.Semi-structured interviews were conducted with a number of key informants, ASSETS participants and non-participants in Kaembeni, and a handful of participants in Mida.Other research methods, used to varying degrees, included transect walks and participant observation.Participation in ASSETS resulted in instrumental learning (task or skills-oriented learning) and communicative learning (understanding what others mean when they communicate with you, understanding, questioning, and negotiating cultural and normative values), as described in the transformative learning theory.Instrumental learning outcomes included: learning new information about the forest and the species within; learning skills related to planting trees; and learning about the connection between deforestation and aridity.Communicative leaming outcomes included confronting local cultural norms and speaking out for conservation.ASSETS participants took a variety of new actions on conservation issues after participating in the program, including planting trees on their farms, starting nurseries, and confronting those involved in illegal activities in the forest.There \¡/as a sharp contrast between ASSETS participants and non-participants with regards to their opinion of the forest; after participating in ASSETS, many people expressed a new and enthusiastic support for the forest.However, ASSETS participants generally had no more ideas about how to "help" the forest than did non-participants, and many participants did not feel that the Arabuko-Sokoke Forest was under threat.IV Thanks to my committee members, Dr.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".