Do They Adapt or Collapse? Digital Immigrants to Digital Communication Technology during the Pandemic of Covid-19
Bibliographic record
Abstract
Communication has permanently been embedded into our life. The pandemic’s temporary erasure of daily communication and interaction has somehow posited people in danger of ostracization, missing out and, ultimately, loneliness. The COVID-19 pandemic was characterised by unprecedented development and the use of digital technologies. The global crisis brought on by the coronavirus pandemic has pushed us further into a digital world, and changes in behaviour are likely to have lasting effects when the economy starts to pick up. The recent experience with COVID-19 shows that the transition to these extraordinary circumstances is far from smooth. More specifically, digital immigrants to ICTs are even more disadvantaged than before. In many cases, the lifeline provided by technologies is only available to those able to access them. Compared to the digital natives, digital immigrants may suffer combined during this transitional digital phase of life and work. Henceforth, this concept paper will thoroughly explain the relationship between social distance and both excellent and negative markers of wellbeing while looking at the nature of digital social interaction through a series of updated literature about technology use among digital immigrants during the pandemic. In addition, the literature review will explain that confidence and competence are vital for learning new things compared to individuals who have not been exposed to technology. Furthermore, the psychological aspect of adopting technologies, which affects the adoption of linked technologies, includes user experience indefinitely. Finally, this concept paper will fill a gap in the literature by exploring the effects of COVID-19 digitalisation on communication and digital immigrants' ongoing technology usage behaviours.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".