Skip to Main Content

Integrating Information Literacy into the Curriculum

Instructional Information Literacy

Misinformation and Fake News

Misinformation, misrepresented information, and outright falsehoods are introduced into the information network daily. The ability to identify when information should be verified as well as the act of verifying information can be overwhelming and difficult. While we mainly think about this as a problem with social media, scholarly literature has its own unique set of challenges for evaluation. Library faculty help students with learning how to think critically about the information they are finding. 

Digital Literacy Gaps Among Students

Digital literacy and information literacy are tied together with the need to access, navigate, and evaluate information being crucial to both literacies.

Bias and Information Literacy

Bias in the Delivery of and Access to Information

Bias in the delivery of information can start early in the information creation process. While we may often think about how mass media delivers information and the bias within that area, there are many ways bias can become part of the delivery of information process in scholarly research as well. Whether a researcher chooses whether or not to publish the results, where the results are published, what kind of media coverage the research receives, and who sponsored the research can all introduce an element of bias into the scholarly work. 

However, once a scholarly work is created and published, accessing the information comes with a separate set of challenges. Algorithms used to search for information may have bias in the way they rank and present the "most relevant" information. The words that are used within research and the words you use to find information may indicate bias. Even the institutions that have been created to promote scholarly activity and discourse are historically biased. 

Bias in Information Organization Systems

Libraries and databases use metadata to describe, organize, and catalog information so that users can find the information that matches their needs. The systems that are created are biased in a way similar to the search algorithms: they are created by humans. Many times a straight cisgender white Christian male is the assumed identity associated with a topic, while other identities are specified outside of the main category. While the intent is to make the information easier to find, it is often historically marginalized groups that are separated out from the "main" heading even when the predominant identities within a category is a historically marginalized group.  

Chronic Discrimination

The library's databases are not censored, meaning that an opinion piece an extremist wrote that was published in a newspaper can be found in the library databases. Therefore it is equally important to know how to identify works, authors, publications, or resources with a history of discriminatory and inaccurate science.

 

Examples of Discrimination in Disguise

In an effort to not promote the work of those listed here, all links are directed to RationalWiki. Please bear in mind that RationalWiki itself is slightly biased, leaning toward liberal ideals. You are encouraged to research each of these examples to reach your own conclusions.