Using Inclusive Language in the Applied-Science Academic Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Language is not neutral or used in a vacuum; language is one of the most powerful tools we have as humans that incorporates personal assumptions, social norms, and cultural ideologies. It is therefore important to consider language critically and to watch for biases in usage. Language reflects the world it is used in, but it is also active in maintaining or redesigning that world. It can be a tool of discrimination or of empowerment. We can use it to foster discrimination, unintentionally or otherwise, or we can use it to help make a fairer world [1]. Words have the power to affect our personhood, our identity, our attitudes, and our images about others. The power of language to affect our identity and behaviour was realized by oppressed groups in the 20th Century. Language is an important part of socialization - it plays a crucial part in the process whereby people learn the behaviours and values of a particular group or culture [2]. Historically, language has left many out. Individuals and groups have been marginalized and discriminated against because of their culture, race, ethnicity, gender, sexual orientation, age, disability, socioeconomic status, appearance, and more. Inclusive language seeks to treat all people with respect, dignity, and impartiality. It is constructed to bring everyone into the group and exclude no one. It is suggested that the basis of communication is not what is said, but how the words are heard. Language framed by derogatory names and symbols can have implications for people and their life experiences [3]. Making changes to use more inclusive language offers us a chance to grow and become better communicators who care for those we are communicating with [4]. This short article is meant to review the concept of political correctness and inclusive language and raise awareness for students and teachers to discriminatory terms that can be easily replaced with clearer and less-offensive alternatives. This topic has been vastly discussed in social sciences and a great number of theories and articles have shed light on the importance of this topic. The goal of this paper is to communicate these ideas to a larger audience including educators in applied sciences including Science, Technology, Engineering and Math (STEM).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it