A guide for using social media in environmental science and a case study by the Students of SETAC
Bibliographic record
Abstract
BACKGROUND: In the past few years, the use of social media has gradually become an important part of our daily lives. While some might see this as a threat to our productivity or as a source of procrastination, social media as a whole have unquestionably changed the way in which information and knowledge disseminate in our society. SOCIAL MEDIA GUIDE: This article is meant to serve as a guide for scientists who would like to establish their online presence and includes an outline of the benefits of using social media as well as strategies for establishing and improving your presence in social media. Environmental scientists in particular can benefit enormously from this approach, since this field of science deals with topics that directly impact our daily lives. CASE STUDY: To highlight these approaches for our fellow scientists in the field of environmental science and toxicology and in order to better engage with our own peers, we describe the outreach methods used by the student advisory councils of the Society of Environmental Toxicology and Chemistry (SETAC) and how we have worked towards an improved social media presence. In this article we present our initiatives to increase social media usage and engagement within SETAC. This includes joint social media accounts organized by the SETAC student advisory councils from various SETAC geographical units. We also led a course on social media usage at the SETAC Nashville meeting in 2013 and are currently developing other outreach platforms, including high school student-oriented science education blogs. CONCLUSION: The Students of SETAC will continue to increase communication with and among SETAC students on a global level and promote the use of social media to communicate science to a wide variety of audiences.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".