Example:
Currency Requirements
Different fields have varying “shelf lives” for information. In rapidly changing fields like technology or medicine, recent sources (within five years) are often crucial. In contrast, historical or philosophical studies may rely on much older sources that retain their relevance.
Rapidly evolving field example: Latest Developments in Artificial Intelligence for Autonomous Vehicles. In this case, a student researching the latest artificial intelligence (AI) technologies for self-driving cars would need recent sources, likely from the past two years. A paper from 2015 about autonomous vehicle AI likely would be outdated, as the field has advanced significantly since then.
Historical studies example: The Causes of the French Revolution.
For this topic, a student could use a mix of recent interpretations and older, classic works. A seminal book on the French Revolution from the 1960s might still be considered a current and valuable source alongside more recent scholarship.
Evergreen Content
Evergreen content remains relevant over long periods. This includes fundamental theories, basic principles, or historical events. Such content can be valuable regardless of its age, especially for providing context or foundational knowledge in a field.
Example 1: Basic Principles of Evolution
A student studying biology could refer to Charles Darwin’s
On the Origin of Species (1859) as an evergreen source for the foundational principles of evolution. While the field has advanced, Darwin’s core ideas remain relevant and are still taught today.
Example 2: Fundamental Theories in Psychology
Maslow’s Hierarchy of Needs, first proposed in 1943, is an example of evergreen content in psychology. While there have been critiques and modifications, the basic theory is still widely taught and referenced in current psychological studies.
Example 3: Basic Mathematical Concepts
A mathematics textbook explaining the Pythagorean theorem could be considered evergreen content. The principle hasn’t changed since its discovery, making even older explanations potentially valuable for current students.
autonomous vehicle AI likely would be outdated, as the field has advanced significantly since then.
