Ethical Storytelling: How Ethical Guidelines Across Canada are Responding to (or Failing to Respond to) Advancements in Multimedia Tools
Bibliographic record
Abstract
This thesis explores the intersection of advancements in multimedia technology and the role of ethics guidelines in Canadian journalism.This thesis uses a quantitative content analysis and semi-structured interviews to build on how virtue ethics, deontology, and utilitarianism interact with the codes of conduct of a sample of Canadian media outlets.This thesis concludes that there are some ethical issues that are arising from new technologies that some journalists identified as outside of the conventional boundaries of ethics guidelines.It identifies that through the multiple ethical systems working in a coordination with one another, organizations can provide their journalists with resources to address the changing technological landscape.This thesis also identifies that a considered reliance on virtue-based ethics will work to future-proof journalism against possible ethical issues related to advancements in multimedia technology.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.043 | 0.033 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".