The New "Good Samaritans": Digital Helpers During Pandemic Times in Canada
Bibliographic record
Abstract
During times of crises such as the present COVID-19 pandemic in Canada, digital helpers emerge as key agents of the promotion of digital literacy in society. Using data from the CPSS-5 national survey, the authors looked at the digital help provided by individuals to various demographic cohorts during the pandemic period in Canada. This survey comprised 3,961 adult respondents aged 15 years old who were interviewed in September, 2020. Digital helpers assisted fellow Canadians in through navigating digital technologies such as videoconferencing, online voice chats, online shopping sites or educational resources. Digital helpers comprised 48% of the total adult population where the most typical form was the assistance of adult respondents aged 18 to 64 years old. Assistance to specific demographic cohorts such as children, teens and seniors varied according to the socio-demographic profiles of helpers. Multivariate analysis of seven typical types of digital help suggests that the likelihood of digital help increased with a younger age of the helper, the presence of a child living at home, university education, urban residence status and/or living in large households. Overall, digital help outcomes appeared to be linked to the life course position of the individual and the types of family or non family networks situated around the helper. The role of young married women living with children and other individuals as the new "Good Samaritans" of digital help is relevant in this regard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".