Sharing sustainability stories: Case study of social media content curation for Canada Research Connections
Bibliographic record
Abstract
What is the best way to break through all the online noise with the vital message of sustainability? This paper details a case study on the strategic use of social media, where content curation tactics are employed to share scientific information related to sustainability. This type of marketing approach is currently under-utilised in both environmental marketing and scientific communication. The study finds that the best practices in the online marketing literature are profoundly useful for spreading sustainability messages to the public via social media platforms. Best practices such as knowing one’s audience, using visuals, maintaining a positive message and providing value make it possible to grow reach, even with a topic that is somewhat dry and unlikely to inspire sharing. In a world where information overload is a pressing concern, content curation is a valuable tactic in every digital communicator’s toolkit, allowing even scientific, technical and sustainability communicators to build communities with relatively low resourcing requirements. This shows how content curation can be highly effective in cutting through internet ‘noise’, even in challenging or non-typical communication situations.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".