Vertical and Horizontal Information flows: The case of SARS (Severe Acute Respiratory Syndrome)
Bibliographic record
Abstract
As high-speed technology connections open the doors to globalization and international exchanges, the potential to exchange a wider range of information across organizational and national boundaries increases. This study explores how the spread of SARS brought to light the potential impact of national culture on time sensitive information exchanges. Due to the rapid spreading and deadly nature of the SARS virus it became critical to mobilize information about the disease within and between national boundaries. These information exchanges can be classified by directionality of flow (vertical or horizontal and within or between organizations or national boundaries) and by the content of the information. This study classifies directionality of flow as vertical or horizontal and within or between countries. The content of the information exchanges was categorized into 3 information domains: 1) information about the spread of the epidemic 2) information about the causal agent of the disease, and 3) information about diagnosis and treatment of the disease. We propose that national culture impacts the directionality, volume, speed, and content of these flows of information. We focus this study on China and Canada (two hot spots for SARs) and investigate information exchange by utilizing secondary source information (newspaper articles and web sites) to map how SARs related information flowed during the period of January 1, 2003 through July 15, 2003.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".