Interpersonal Communication Between Transgender and Cisgender People: A Polish-Canadian Comparison
Bibliographic record
Abstract
Interpersonal communication between transgender/gender-diverse and cisgender people is an understudied area within the fields of communication studies and gender studies; studies on Canadian and Polish interpersonal communication dynamics involving these demographic groups are even rarer. The purpose of this research project therefore was two-fold. On an applied level, this study sought to document and analyze interpersonal communication dynamics between transgender/gender-diverse and cisgender people in Poland and Canada, respectively. On a theoretical level, this study sought to extend interpersonal transgender communication theory. Based on in-depth interviews conducted with 44 participants in Canada and Poland, the authors identified common positive and negative interpersonal communication experiences between transgender/gender-diverse and cisgender people within each nation. The thematic analysis uncovered similarities and differences; quotations from the interviews allowed for identification of concrete moments in interpersonal communication encounters. Findings were analyzed from the perspective of recent articulations of communication accommodation theory (CAT) with particular emphasis on the role of accommodation and nonaccommodation, affective motives, and skills training. The relevance of accommodative communication behavior and the significance of perceived communicative intent emerged as key findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".