Social media’s potential to promote conservation at the local level: an assessment in eleven primate range countries
Bibliographic record
Abstract
Abstract Historically, Internet access has been linked to a country’s wealth. However, starting a decade ago, this situation changed dramatically and Internet access became increasingly available in primate range countries. The rapid growth of smartphone use in developing nations has created new avenues to communicate conservation. Here we assess the potential of social media to promote primate conservation at the local level within primate range countries. We interviewed 381 people in communities associated with 18 conservation projects from 11 countries to assess their use of social media. We found that 91% of the people had at least one social media account and 95% of these people checked their accounts daily. The median number of contacts per person across all platforms was 453 and 300 considering only each person’s most used platform. We also documented that local conservation projects had a diversity of information they wanted to relay to the local community through social media. Our research highlights the potential for social media to be an extremely useful communication tool for tropical conservation scientists. Thus, we encourage more conservation groups to explore using social media to communicate to local communities and to report on the impact it has on conservation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".