Code-Switching in the Workplace: The Strategies, Challenges, and Practical Implications
Bibliographic record
Abstract
In recent decades diversity in organizations has dramatically increased. This movement is due to changing demographics in the workforce, societal and political shifts that promote equal rights and opportunities, and trends in globalization that increase connections among people from different parts of the world. The increase in diversity has profoundly affected how people in organizations interact. Without question, the most fundamental aspect of these interactions involves communication, which forms the backbone of interpersonal exchanges in and around the workplace. Communication drives business and is a critical aspect of organizational effectiveness (Snyder & Morris, 1984). It is critical to note that people from diverse backgrounds bring different approaches to communication into organizations. When people with varying communication styles interact, we often see dynamic patterns of mutual adaptation that can vary on multiple dimensions. These patterns form the basis of the concept of code-switching. Undoubtedly, not only does communication drive business, code-switching drives business. The papers presented in the symposium come from different perspectives of how code-switching exists and impacts people in the workplace and they collectively extend literature theoretically and empirically. By improving our understanding of code-switching, these papers increase awareness with regards to issues of diversity, equity, and inclusion, and provide insight into what skills business organizations and managers of the future need. With this research, in alignment with the theme, “creating a better world together,” the authors seek to provide a new awareness for organizations and managers and scholars of management. Furthermore, the authors believe the insight provided here will provide an additional opportunity for managers and businesses to consider resetting organizational practices that enable the creation of new future organizational arrangements. The authors believe code-switching awareness and skills can be our bridge to efficiency, productivity, and overall workplace inclusion. What does it mean to talk the talk? A Content Analysis of Code-Switching Literature Presenter: Rose Brown; Cornell U. Underpinnings of Code-Switching: An Observational Study of Black Americans’ Code Switching Tendency Presenter: Darin Johnson; U. of Pennsylvania Code-Switching as a Survival Mechanism: Implications for Black Employees Presenter: Jaylon Sherrell; Harvard Business School How and When Workplace Ostracism Impacts Code-Switching Behaviors Presenter: Patrick Plummer; Howard U.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".