DIGITAL DIVIDE: UNDERSTANDING DIFFERENCES IN ICT LITERACY IN THE CANADIAN CONTEXT
Bibliographic record
Abstract
We know that inequality in access to the Internet is significantly related to socio-demographic inequalities, but we know far less about how other aspects of the “digital divide”, such as information and communication technology (ICT) literacy, are related to these same inequalities. The present study uses a sample of 21,189 Canadian respondents to the Program for the International Assessment of Adult Competencies (PIAAC) survey, to examine relationships between sociodemographic factors and ICT literacy and how those relationships differ by age. ICT literacy is measured as respondents’ scores on the PIAAC problem solving in technology-rich environments module. Results indicate that baby boomers scored significantly lower on the ICT literacy scale than younger counterparts. Results of multiple regression analyses are consistent with prior research on the digital divide indicating that social disparities in Canada in ICT literacy are related to gender, education, income, type of occupation, immigrant status, language spoken at home, and everyday experience with computers. When these relationships are examined across age groups, findings consistently indicate that being a man and having a skilled job or higher education are significantly and positively related to ICT literacy. However, older adults were found to be more disadvantaged in terms of ICT literacy by lower educational attainment than their younger counterparts while women aged 25+ are more disadvantaged than women under age 25 compared to their male counterparts. Overall, our findings show that the risk factors for lower levels of ICT literacy are similar to those for poorer access to the Internet.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".