The digital age has improved access to large, comprehensive textual data sources and enabled the extraction and coding of high-dimensional, complex data. This data’s richness and magnitude (i.e., “Big”-ness) requires innovative computational and statistical algorithms for analysis. Especially, the idea of semantic networks has a long history in social sciences, with a current renewal of interest with new methodological innovations, such as Machine Learning (ML) and Natural Language Processing (NLP). A recent line of research applies network science drawing on graph theory to explore how knowledge is constructed in the mental lexicon. This study investigates how semantic network and word embedding algorithms can be used in parent-child language interaction analysis. Potential implications of these findings for research and practice will be discussed. 

Objectives 

• Regarding the conference theme “The Science of Families: Nurturing Hope, Happiness, & Health,” this presentation will help early childhood stakeholders embrace the power of “Big Data” to tailor evidence-based practices to fulfill local needs and circumstances of ALL children and their families and tackle pressing social justice challenges.
• Language is social in nature. It is the primary means by which we gain access to the contents of others’ minds. With an emphasis on the sociocognitive processes of development and learning, these situational analyses specify a speaker/writer, listener/reader (in a role relationship), a time, a topic, and a place. This work has explored how these contextual elements influence everyday interactions between a child and people, objects, and symbols. 
• This presentation will discuss the usefulness of innovative statistical and computational techniques, such as Machine Learning (ML) and Natural Language Processing (NLP), in parent-child language interaction analysis.  

Population Codes: early childhood, diverse but not representative

Method and Approach Codes: content analysis, cluster analysis

Summary
Many scholars have argued that Aboriginal research should be carried out in ways that are respectful and ethically sound vis-à-vis Indigenous perspectives. The current study explored classroom preparation(s), research (in-the-field) learnings, and advice regarding how to effectively engage in collaborative investigations with Indigenous groups. A snowball sampling method was used to recruit 14 participants (3 Indigenous community elders, 11 non-Indigenous students) who have taken part in and/or supervised research within Native communities. Preliminary data analysis revealed the importance of building trust, practicing observant behaviors, and being attentive to the diversity across Native communities. The study can contribute in building trustworthy and transparent relationships between researchers and said communities.Building on the strengths of this participant-researcher relationship, researchers can also interpret study results in ways that are more reflective of Indigenous perspectives.

Objectives

• To explore key learning experiences (classroom and in-the-field) of non-Indigenous students/new professional researchers in collaborating with Indigenous communities.
• To highlight advice from the community elders about appropriate research strategies for novice non-Indigenous researchers while collaborating with Indigenous groups.
• To propose best methodological practices that student researchers can adopt while conducting research with Native communities.

Subject Codes: inclusion, communities, ethnicity
Population Codes: American Indian/Alaskan Native/First Nations, Family Scientists, indigenous
Method and Approach Codes: qualitative methodology, grounded theory, best practices

 Summary
Decentering Whiteness in family science coursework requires rethinking how family science pedagogy facilitates critical self-reflexive practices for faculty and students. In order to shift toward a sustainable racially emancipatory praxis, instructors must create a “no niceness” space for exposing the dominant discourses that normalize Whiteness in family coursework. As three BIPOC scholars, we use critical race counterstories to resist the master narrative of colorblind “rigorous methods” that complicated the use of emancipatory research methods in a family science course taught by the first author in Fall 2020. Our counterstories reveal the difficulty encountered at multiple levels—epistemic barriers, curricular constraints, and a lack of structural scaffolding such as integrated preparation courses that would systematically introduce the language and concepts of critical race frameworks to all learners. These difficulties and the tolerance for colorblind language used by White students created a tenuous situation for teaching and learning critical race research methods.

Objectives

• To demonstrate the use of Counterstory as a critical race methodology that centers the experiences of BIPOC students and faculty engaged in teaching and learning critical research strategies in a mainstream family science core-course
• To identify concrete classroom practices such as colorblind discourse and White silence during discussions of racism that impede instructors' and students' ability to engage in the teaching and learning of critical methods and critical race tools
• To identify directions for curricular change in family science coursework that would support a scaffolded approach toward preparing all students to understand the language and concepts of critical and critical race methodologies

Subject Codes: racism, diversity, education
Population Codes: educators, People of Color, Family Scientists
Method and Approach Codes: community participation/action research, qualitative methodology, educational