Saturday 8th February, 2025: Rodney Croome, Calls for Australia to follow NZ in Lifting Blood Donor Ban
Bibliographic record
Abstract
Macca and Paul talk to Rodney Croome, spokesperson for Let Us Give offered congratulations whilst calling for change here in Australia. In Australia the Let Us Give campaign are calling for Australia to follow suit, praising the changes in New Zealand.Currently Australians face the same ban on donating blood and plasma as those in New Zealand.\"Gay men, bisexual men, transgender women and some non-binary people who have sex with men, are expected to abstain from sex for three months before giving blood because of outdated fears about HIV infection\" they state on their website.\"We congratulate New Zealand's blood authorities for taking a step that will mean a new source of safe for New Zealanders in need and less discrimination in blood collection\" he said.\"New Zealand now joins the UK, US, Canada and an increasing number of other countries that assess all donors for their individual sexual risk, rather than barring entire groups regardless of individual risk.\"\"We urge Australia's blood authorities to follow New Zealand's lead as quickly as possible, or risk being seen to maintain a blood collection system that is archaic, inefficient, discriminatory and out-of-step\" he concluded.https://www.starobserver.com.au/news/calls-for-australia-to-follow-nz-in-lifting-gay-blood-ban/235101https://www.letusgive.com.au/
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.461 | 0.287 |
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".