Thematic Article
Challenges for expanding inventories of climate possibilities through indigenous and local knowledges in rural Zambia
,
mmatenga@yahoo.com
ee15srs@leeds.ac.uk
Abstract
This article explores the integration (or marginality) of indigenous and local knowledge (IKL) in donor-driven community climate adaptation (CDCA) projects and the extent to which this helps expand inventories of adaptation possibilities for host communities and strengthen climate adaptation resilience in Zambia. Through multi-level qualitative research design, this study reveals that, even where climate interventions are intentional about being inclusive of community knowledge, they are likely to promote policy-centric knowledges and interventions that invisibilises ILK. Empirical evidence shows the application of CDCA expresses top-down assumptions of livelihood resilience and embeds uncritical views of what community is, including what might be socially and culturally appropriate forms of adaptation. CDCA implementation strategy is exclusionary and misaligned with ILK, affecting possibilities of knowledge intersection. This article elucidates how climate adaptation that marginalises ILK fails to expand inventories of climate adaptation possibilities for communities supposed to be adapting and proposes how this gap could be bridged. (This article is published in the thematic collection ‘African ecologies: the value and politics of indigenous knowledges’, edited by Adriaan van Klinken, Simon Manda, Damaris Parsitau and Abel Ugba.)
Keywords
community-driven climate adaptationindigenous and local knowledgeclimate adaptation resilienceinventories of adaptation possibilitiesZambiaCopyright statement
© The author(s) 2024. This is an open access article licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International LicenseCite this article
Manda, S., Matenga, C., Mdee, A., Smith, R. & Nkiaka, E. (2024), ‘Challenges for expanding inventories of climate possibilities through indigenous and local knowledges in rural Zambia’, Journal of the British Academy, 12(3): a31 https://doi.org/10.5871/jba/012.a31crossref
dimensions
Article metrices
View count: 106
Download count: 4
No Data Found
No Data Found
No Data Found
This paper examines whether artificial intelligence industry developers of large language models should be permitted to use copyrighted works to train their models without permission and compensation to creative industries rightsholders. This is examined in the UK context by contrasting a dominant social imaginary that prioritises market driven-growth of generative artificial intelligence applications that require text and data mining, and an alternative imaginary emphasising equity and non-market values. Policy proposals, including licensing, are discussed. It is argued that current debates privilege the interests of Big Tech in exploiting online data for profit, neglecting policies that could help to ensure that technology innovation and creative labour both contribute to the public good.
Philosophy is being hit hard by the decline in university funding, thanks in particular to the lack of a significant overseas student market and the reliance of many departments on a large number of individually small joint courses, which universities are keen to axe as a cost-cutting measure. One—admittedly modest—way in which the situation can be ameliorated, and which is working well at Leeds, is to offer bespoke teaching to university science and medical departments. Such departments want, and often need for accreditation purposes, to teach their students about (for example) ethics and sustainability, and they see the benefits for student engagement and employability in incorporating some relevant humanities teaching and assessment. So this is one way in which philosophy departments in particular, but perhaps humanities departments in general, might try to keep their heads above water. This article is published in the thematic collection ‘On recent closures and threats of closure in the Humanities and Social Sciences’, edited by Regenia Gagnier.
No Data Found
