Ethical challenges in an evolving digital communication era: coping resources and ethics trainings in corporate communications
Bibliographic record
Abstract
Purpose This study is motivated to investigate the ethical challenges facing public relations professionals in today's digital communication environment. Specifically, the authors focused the research on the new ethical challenges in digital practice, the resources relied on when encountering ethical challenges and public relations professionals' efforts in seeking trainings on communication ethics. Design/methodology/approach An international online survey was designed and conducted in Canada and the USA. The final sample includes 1,046 respondents working full time in the profession of public relations and communication. In addition, the authors prespecified several demographic quotas in sampling design in order to recruit a more representative sample. Findings The research found nearly 60% of surveyed professionals reported that they faced ethical challenges in their day-to-day work, and there is a wide range of ethical challenges in digital practices. Results also revealed that professionals use various resources to deal with ethical issues. Those resources include ethical codes of practice of professional associations, ethical guidelines of their organizations and their personal values and beliefs. As common as experiencing ethical challenges, over 85% of surveyed professionals reported that they have participated in communication ethics training. However, only 30% of participants indicated that their ethics training took place in the past year. Originality/value The research provides solid evidence that the digital communication environment generates more ethical challenges, while it creates new ways of delivering content in corporate communications. Professional associations and organizations shall dedicate efforts in providing timely ethics training to PR professionals at all levels of leadership within and beyond corporate communications.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".