Twitter Analytics to Inform Provisional Guidance for COVID-19 Challenges in the Meatpacking Industry
Bibliographic record
Abstract
The COVID-19 pandemic raised considerable challenges to obtain reliable guidance to help occupational health practitioners, workers, and stakeholders building up efficient prevention strategies at the workplace, between the constant increase of publications in the domain, the time required to run high-quality research and systematic reviews, and the urgent need to identify areas for prevention at the workplace. Social Media and Twitter, in particular, have already been used in research and constitute a useful source of information to identify community needs and topics of interest for prevention in the meatpacking industry. In this commentary, we introduce the methods and tools we used to screen relevant posts on Twitter. Twitter analytics is a way to capture real-time concerns of the community and help ensure compliance with the notion of social accountability. As such research has limitations in terms of exhaustiveness and level of evidence, it should be considered as provisional guidance to direct both actions at the workplace and further conventional research projects.
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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 | 0.053 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".