Vol. 1 No. 1 (2021)

View Issue TOC

Candidate Selection Processes in African Political Parties: Democracy Deficits and Elite Control: Climate Change Dimensions

Abraham Kuol Nyuon, Associate Professor of Politics, Peace, and Security
DOI: 10.5281/zenodo.19551431
Published: November 2, 2021

Abstract

This article examines Candidate Selection Processes in African Political Parties: Democracy Deficits and Elite Control: Climate Change Dimensions with a focused emphasis on Ethiopia within the field of Political Science. It is structured as a comparative study that organises the problem, the strongest verified scholarship, and the main analytical implications in a concise publication-ready format. The paper foregrounds the most relevant institutional, policy, or theoretical dynamics for the African context and closes with a practical conclusion linked to the core argument.

Full Text:

Read the Full Article

The HTML galley is loaded below for inline reading and better discovery.

How to Cite

Abraham Kuol Nyuon (2021). Candidate Selection Processes in African Political Parties: Democracy Deficits and Elite Control: Climate Change Dimensions. African Legislative Studies (Political Science focus), Vol. 1 No. 1 (2021). https://doi.org/10.5281/zenodo.19551431

Keywords

Candidate Selection ProcessesAfrican Political PartiesPolitical Parties DemocracyParties Democracy DeficitsElite Control ClimateControl Climate Change

Research Snapshot

Desktop reading view
Language
EN
Formats
HTML + PDF
Publication Track
Vol. 1 No. 1 (2021)
Current Journal
African Legislative Studies (Political Science focus)

References

  • Houlihan, E.C., & Underwood, W. (2021). Emergency Law Responses and the Covid-19 Pandemic (Global State of Democracy Thematic Paper 2021).
  • McCoy, J., & Somer, M. (2021). Overcoming Polarization. Journal of democracy.
  • Pereira, T., & Freire, T. (2021). Positive Youth Development in the Context of Climate Change: A Systematic Review. Frontiers in Psychology.
  • Pettersson Ruiz, E., & Angelis, J. (2021). Combating money laundering with machine learning – applicability of supervised-learning algorithms at cryptocurrency exchanges. Journal of Money Laundering Control.