Bibliographic record
Abstract
This paper analyzes the evolution of the digital divides in Canada. Our first model suggests that education, age, income, geographical location, sex and employment status all influenced Internet use in 2012, after controlling for all the factors. The descriptive statistics suggest that the digital divide based on Internet use from any location decreased slightly, generally speaking, between 2010 and 2012. However, when we look at Internet use with a wireless handheld device, the digital divide has widened considerably in most socio-demographic groups. A linear regression model is then used to assess the presence of a “second level” digital divide by regressing the number of online activities performed by Internet users on a set of socio-demographic variables. The results suggest that income, education and age are the main predictors of the number of activities Internet users carry out online, thus suggesting that younger, wealthier and more educated individuals have a greater propensity to take advantage of our digital society. It is important for policy analysts and academic researchers to continue monitoring the various aspects of the digital divide in order to ensure all Canadians have the opportunity to participate fully in a digital society. However, such monitoring requires pertinent data and measurement of Internet use in Canada remains somewhat ad hoc. For instance, this and other similar research efforts use data from the Canadian Internet Use Survey (CIUS), which continues to be conducted on an occasional basis. This paper presents some thinking on how the program of measurement of the digital divide and more broadly the measurement of the digital economy can evolve in Canada. It addresses a series of data gaps pertaining to the measurement of the digital divide, from issues of population coverage to barriers to adoption and use, to digital skills and competencies, ICT use in school, and effects of digital platforms on connecting Canadians, as well as impacts of increased information flows. Key elements of a roadmap for the measurement of the digital divide in Canada are identified, including strengthening of partnerships as leader and curator of official statistics; making more efficient use of existing survey infrastructure; targeting content on individual use of ICTs; and exploring avenues for direct measurement of Internet performance. The paper concludes with a series of key considerations for addressing the digital divide in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".