Bibliographic record
Abstract
MacGillivray Freeman Films was founded over forty years ago by Greg MacGillivray and the late Jim Freeman. In 2011, the company launched “the world’s largest ocean media campaign, a 10-year global initiative called One World One Ocean” (MacGillivray Freeman Films, 2010, Our History, para. 10), an awareness and change campaign focusing on saving the world’s oceans. The mission of One World One Ocean (OWOO) is to use “the power of film, television, new media and education initiatives… to change the way people see and value the ocean — and motivate action to restore it” (OWOO, 2012, Mission, para. 4). One World One Ocean’s science advisors, including principal advisor Dr. Sylvia Earle, believe that “the ocean is at a tipping point…. our actions over the next 10 years will determine the state of the ocean for the next 10,000 years” (OWOO, 2013, Why the Ocean?, para. 3). The media types used in the organization’s campaign were chosen because MacGillivray Freeman Films wants to develop and expand on its film-industry successes. This article outlines the history of One World One Ocean and explores its mission, its history, its scientific basis, its current projects and initiatives, its successes to date, and its future goals. It explains why these media platforms were chosen to support the organization’s mission and explores the vital questions of why it is important for all of us that we save the world’s oceans and how this mammoth task can be tackled before it is too late. The purposes of this article are to inform readers about One World One Ocean and to inspire them to consider ways they can work to achieve the organization’s crucial goals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".