SUFB 1204: The trouble with non-profit organizations in marine conservation
Bibliographic record
Abstract
I am listening to a podcast series called White Saviors that is produced by Canadaland. It's a series on the rise and fall of a charity called the We Charity that was founded by two brothers (one of which went to my high school in Toronto, Canada) that wanted to save children from child labor.The organization got huge and that's where it went wrong. Now the organization is trenched in scandal and an ongoing legal investigation. The series made me think of all of the people in marine conservation that start out with a notable mission, but end up on the wrong side of that mission. Check out all of our episodes on www.speakupforblue.comWant To Talk Oceans? Join the Speak Up For Blue Facebook Group: http://www.speakupforblue.com/group.Speak Up For Blue Instagram: https://www.instagram.com/speakupforblue/Speak Up For Blue Twitter: https://twitter.com/SpeakUpforBlue
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.084 | 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; both teacher heads agree on what is shown here.
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".