Analysis of the Science and Technology Narrative within Organ Donation and Transplantation Coverage in Canadian Newspapers
Bibliographic record
Abstract
Organ failure is one cause of death. Advancements in scientific research and technological development made organ transplantation possible and continue to find better ways to substitute failed organs with other organs of biological origin or artificial organs. Media, including newspapers, are one source of information for the public. The purpose of this study was to examine to what extent and how science and technology research and development are covered in the organ transplantation and organ donation (ODOT) coverage of n = 300 Canadian newspapers, including the two Canadian newspapers with national reach (The Globe and Mail, National Post). The study generated qualitative and quantitative data addressing the following issues: (1) which scientific and technological developments are mentioned in the ODOT coverage; and (2) what issues are mentioned in the coverage of scientific and technological advancements linked to ODOT. We found little to no coverage of many technological and scientific advancements evident in academic and grey literature covering ODOT, and we found little engagement with social and ethical issues already raised about these advancements in the literature. The only area we found to be covered to a broader extent was xenotransplantation, although the coverage stopped after 2002. We argue that the newspaper coverage of ODOT under reports scientific and technological advancements related to ODOT and the issues these advancements might raise.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".