Bibliographic record
Abstract
New developments in information and communication technology (ICT) such as the growth of Internet use, have been portrayed as an innovative medium of information exchange and thus providing new opportunities to rural Canadians. However, recent studies have shown that fewer rural Canadians were using the Internet compared to urban Canadians (Thompson-James, 1999; McLaren, 2002). The purpose of this study is to estimate and to analyze the determinants of Internet use by Canadians in order to understand the factors associated with lower Internet use in rural Canada with specific emphasis on whether "rurality" acts as an independent factor on Internet use. A logit model using the "Household Internet Use Survey" (HIUS) from 1998 to 2000 is used to analyze various socioeconomic determinants such as age, household income, location, self-employment and education. Our research indicates that although factors such as low income and an older population restrict Internet use by rural Canadians, "rurality" per se also appears to be a constraint on Internet use in Canada. It is necessary to analyze and understand the determinants of Internet use since this can help public and private agencies in customizing and altering information infrastructure, which can help in increasing Internet use among rural Canadians.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".