The impact of <scp>ICT</scp> development on economic resilience during the <scp>COVID</scp>‐19 pandemic: A country level analysis
Bibliographic record
Abstract
Abstract This research explores the relationship between information and communications technology (ICT) development and its impact on a country's economy during the COVID‐19 pandemic. This study has two primary objectives: (1) to understand how ICT development influences a country's economic resilience during a crisis, and (2) to examine the interrelationships between various country‐level ICT development measures. We use multi‐year, country‐level data made available by the United Nations, the World Bank, and World Health Organization to empirically examine our research model. Partial least squares path analysis is the primary research methodology employed in this study. Our results suggest that ICT development has a positive impact on economic resilience in the face of the COVID‐19 pandemic. In addition, our results specify the interrelationships between individual ICT development measures and economic resilience. This research contributes to the extant body of knowledge on the impact of country‐level ICT development on economy by empirically validating a research model that explores the relationships between the various measures of ICT development and economic resilience of countries during the COVID‐19 pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".