Bibliographic record
Abstract
Abstract Social media platforms have played key roles in pandemic communication. Similarly, translation has also played key roles in the dissemination of critical public health information to the masses. The plural “roles” is deliberate: platforms and translation have amplified not only vetted experts and science, but misinformation and disinformation as well. Relatedly, different types of social media accounts fulfill different roles, too: from enabling the sharing of opinions or entertainment content (e.g., meme accounts), to providing factual, evidence-based information (e.g., science communication accounts). In this chapter, we examine three different types of *translation ( Tymoczko [2010] 2014 ) exemplified by three different Manitoban social media accounts on Instagram: @icimanitoba (interlingual and cultural translation); @mbpolidragrace (intersemiotic, intercultural, and knowledge translation); @mbcovid19updates (intersemiotic and knowledge translation). Part of the novelty of this case study is that it looks at an understudied Canadian province: Manitoba. As such, the case study contributes to the growing body of scholarship on the pandemic in Canada by providing Manitoban social media data and analyses. This work also has resonance for international audiences, given that memes, data visualizations, and other forms of *translation examined here are used on a global scale; as such, our data could be used for comparative analyses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".