Fostering Equity and Diversity Through Essential Mixed Methods Research Inclusive Language Practices
Bibliographic record
Abstract
The words we choose to use as mixed methods researchers are the threads that weave the fabric of our collective knowledge.Embracing inclusive language ensures that every thread is diverse, strong, and reflective of the rich tapestry of human experience that together enriches our global mixed methods research community. Importance of Inclusive Language Use for Mixed Methods ResearchLanguage plays a pivotal role in creating welcoming and accessible research spaces.The selection and use of words significantly influence the atmosphere and inclusivity of our mixed methods research community spaces.As mixed methods researchers, educators, and authors, word choices impact everyday interactions with study participants, learners, and readers because inclusive language signals respect for differences and belonging for all.Inclusive language refers to words and phrases that acknowledge diversity and promote equality and respect toward all people.As an example, consider the use of "firefighter" instead of "fireman" to acknowledge and include everyone within the firefighting profession.By being mindful of the impact words and phrases have on others, language choices can avoid excluding, stereotyping or marginalizing individuals or communities.Inclusive language is a moral compass that guides us toward a more equitable and diverse intellectual landscape with far-reaching consequences for our global mixed methods research community.As a result, the language we use as a global mixed methods research community is essential for advancing our commitments to equity, diversity, and inclusion in our studies and our interactions with one another.Using inclusive language in our mixed methods research studies and various interactions can actively promote more welcoming and accessible spaces, which, in turn, encourages individuals from diverse backgrounds to participate and contribute to methodological practice and societal advancements.While I may not be perceived as the ideal individual to facilitate this conversation, it's crucial that our global mixed methods research community engage in this dialogue.I welcome the opportunity to learn from others; the ideas presented below are derived from my lived experiences working with diverse populations and my broad reading of relevant literature.The ideas are intended to reflect my ongoing commitment to advancing equity, diversity, and inclusion
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.116 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".