CULTURAL DIVERSITY IN CLASSES AND WORKPLACES: THE COMMUNICATION CHALLENGES
Bibliographic record
Abstract
Dr. Peruvemba S Jaya is an Associate Professor in the Department of Communication, Faculty of Arts, at the University of Ottawa. Prior to that, she has been in teaching in faculties of business in the USA and Canada. She has a PhD in Business Administration (Organizational Behaviour and Organizational Studies) from the University of Rhode Island, USA, MA in Sociology from the Delhi School of Economics, University of Delhi, India and BA (Hons) in Sociology from the University of Delhi, India. Her research interests include the areas of gender diversity and multiculturalism in the workplace, immigration and gender, immigrant women, South Asian immigrant women’s experience, immigrants’ issues, interpersonal communication, identity formation and construction processes, postcolonial theory, and intercultural communication. She is also interested in ethnic media and qualitative research methodologies. She is affiliated with the Institute of Women’s Studies, University of Ottawa as well as Affiliate Faculty in the interdisciplinary E Business and Technology PhD Program. She is a member of the Organizational Communication Research Group of the University of Ottawa, Department of Communication. She is currently the Regional Representative of Research Committee 32: Women in Society of the International Sociological Association and a Member of the Board of the Canadian Communication Association.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".