“Staycation” attitudes and experiences during the COVID-19 pandemic: an analysis of Twitter social media data across different international regions
Bibliographic record
Abstract
Michael Lang is an Associate Professor in Business Information Systems at the School of Business & Economics, National University of Ireland, Galway. He obtained his Bachelors degree from University College Dublin, Higher Diploma and Masters degree from NUI Galway, and PhD from University of Limerick. Manik Lochan is a Business Intelligence Analyst with MiFinity. He obtained his Bachelor of Technology degree from Jaypee Institute of Information Technology, India and his M.Sc. in Business Analytics from National University of Ireland, Galway. Nanda Kumaran Unni Nair is a graduate of National University of Ireland, Galway where he completed his M.Sc. in Business Analytics in 2021 under the supervision of Dr. Michael Lang. Venkatesh Srinivasan is a Data Analyst with Spanish Point Technologies. He obtained his Bachelors degree from Sri Venkateshwara College of Engineering and Technology, India and his M.Sc. in Business Analytics from National University of Ireland, Galway. Ankith Kumar Makam Venugopal is a Service Delivery Engineer with SAP. He obtained his Bachelors degree from SJB Institute of Technology, India and his M.Sc. in Business Analytics from National University of Ireland, Galway.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".