The use of indigenous knowledge in development: problems and challenges

The use of indigenous knowledge has been seen by many as an alternative way of promoting development in poor rural communities in many parts of the world. By reviewing much of the recent work on indigenous knowledge, the paper suggests that a number of problems and tensions has resulted in indigenous knowledge not being as useful as hoped for or supposed. These include problems emanating from a focus on the (arte)factual; binary tensions between western science and indigenous knowledge systems; the problem of differentiation and power relations; the romanticization of indigenous knowledge; and the all too frequent decontextualization of indigenous knowledge

The 4IR and the Humanities in South Africa

The world is at a cross-roads because of industrial change, compounded by a global pandemic. Humanities and social science education is grappling with the meaning of this change, to the effect that there have been some anxieties and misguided perceptions about the irrelevance of the humanities in this emerging new world. With the emergence of new technologies, this book highlights the indespensible centrality of humanity and the humanities going forward. The book will provide a reference point for new and innovative approaches to the humanities in the 4IR in South Africa and Africa. Its diverse content means that it will be of use across the spectrum of humanities and social science

Operationalizing local ecological knowledge in climate change research : challenges and opportunities of citizen science

Current research on the local impacts of climate change is based on contrasting results from the simulation of historical trends in climatic variables produced with global models against climate data from independent observations. To date, these observations have mostly consisted of weather data from standardized meteorological stations. Given that the spatial distribution of weather stations is patchy, climate scientists have called for the exploration of new data sources. Knowledge developed by Indigenous Peoples and local communities with a long history of interaction with their environment has been proposed as a data source with untapped potential to contribute to our understanding of the local impacts of climate change. In this chapter, we discuss an approach that aims to bring insights from local knowledge systems to climate change research. First, we present a number of theoretical arguments that give support to the idea that local knowledge systems can contribute in original ways to the endeavors of climate change research. Then, we explore the potential of using information and communication technologies to gather and share local knowledge of climate change impacts. We do so through the examination of a citizen science initiative aiming to collect local indicators of climate change impacts: the LICCI project (www.licci.eu). Our findings illustrate that citizen science can inspire new approaches to articulate the inclusion of local knowledge systems in climate change research. However, this requires outlining careful approaches, with high ethical standards, toward knowledge validation and recognizing that there are aspects of local ecological knowledge that are incommensurable with scientific knowledge

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Funding: H2020 European Research Council (Grant Number(s): GA 101044975, GA 101098020).Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables: Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.Peer reviewe

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Motivation. Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included. The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain. Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain. The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement. The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format. csv and. SQL

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL

Farm labour in California

Farm labour, Californi

Farm labour in California

Farm labour, Californi