Transformative learning through conservation : a case study of the Arabuko-Sokoke Schools and Eco-Tourism Scheme, Kenya
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,020 | 0,004 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».