"Save the Barrier Reef" from 1967 to 2013: a forty six year old campaign that changed the Great Barrier Reef
Bibliographic record
Abstract
In 2013 the Great Barrier Reef hit the headlines with political debates on port expansion, dredging, UNESCO World heritage status, and tourism. From Greenpeace to conservationist Bob Irwin and octogenarian activist June Norman, environmentalists carried out media tours the length of Queensland in a campaign to 'Save the Reef'. Save the Reef '2013' came 46 years after the first campaign. In 1967 conservationist and philanthropist, John Busst, with the support of the Wildlife Preservation Society of Queensland, Queensland Littoral Society (now Australian Marine Conservation Society, AMCS) and then Prime Minister, Harold Holt, successfully prevented the Reef from being mined. The first ever 'Save the Reef' campaign successfully halted a Cairns sugar farmer's application to mine Ellison Reef for limestone. The argument for mining was that the reef was dead following a crown-of-thorns outbreak, which activists countered with scientific discourse and questions of ownership and intrinsic value. Based on archival research and impressions from a group interview with key environmental activists of the time, this paper will be an initial exploration of the Save the Reef campaign. We will examine how the 1967 campaign engaged with the media to situate the campaign in both global and local contexts. Through this historical lens, we will pose questions about the media strategies of today's environmental campaigns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".