Water up to our necks: learning and responses to hydroclimatic variability in Brazilian Amazon floodplain communities
Notice bibliographique
Résumé
Understanding how floodplain communities of the Brazilian Amazon respond to the impacts of extreme flooding induced by hydroclimatic variability and how learning supports these responses are the dual focus of this thesis. The UN Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (2014) demonstrates that rural communities in developing countries are among those most impacted by extreme climatic events, which are likely to increase in frequency and intensity in the near future. However, the community-based adaptations (CBA) literature indicates that rural communities have coped with climate variability by using a range of local assets, especially when governments have failed to provide proper assistance. My study followed a qualitative approach, employing semi-structured interviews with community members and institutional agents, participant observation, participatory mapping exercises, and validation workshops. Findings demonstrate that the repeated occurrence of extreme floods between 2009 and 2015 resulted in severe impacts, including some that had never been experienced by the local communities, such as the complete loss of perennials. Utilizing the sustainable livelihoods and resilience lenses, I investigated the locally-devised short-term and long-term responses to these impacts. Results revealed a wide range of responses, some of which I placed in a newly-proposed category of annual responses. Data about the capacity to absorb impacts without responding and about transformative responses were also provided. I also found that much of the learning that was foundational to the responses was instrumental in nature. The learning outcomes for individual participants resulted in proposing two new learning domains –introspective and emancipatory learning. Transformative outcomes were revealed for some participants who found that the intensity and repetition of extreme flooding drove them to leave the floodplain for upland or urban areas. Findings also revealed a wide array of learning domains and sources of individual learning, such as experience, dialogue, reflection, and observation, that contributed to expanding the applicability of the transformative learning theory. Lessons drawn from community experiences on how to live with hydroclimatic changes demonstrate that continuous learning through multiple sources is essential for helping local people increase their capacity to overcome uncertainties. Learning is also fundamental for communities to build a wider range of possible responses to be chosen from and applied with agility in order to decrease vulnerability to increasingly variable, dynamic and unpredictable impacts.
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 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 | 0,004 | 0,013 |
| 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 | 0,006 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».