Differential IT Access and Use Patterns in Rural and Small-Town Atlantic Canada
Bibliographic record
Abstract
Popular press and government rhetoric suggest that there has been steady progress in the extent to which individuals, households, businesses, organizations, and communities are using the Internet as part of their daily lives (Bruce, 1998, 1997). However, there is little empirical evidence to support this claim. In this chapter I argue that there has been slow and uneven penetration of Internet use in rural and small-town communities in Atlantic Canada, despite the best efforts of policies and programs. Drawing on evidence from a recent Internet use survey, suggestions are made for improving the performance of policies and programs aimed at increasing Internet access and use. The purpose of this chapter is to provide an empirical overview of differential Internet access and use patterns, using data collected from a January 1998 survey (Jordan, 1998; Bruce and Gadsden, 1999) of 1501 households in 20 different Atlantic Canadian communities grouped into five distinct “community categories.” (Reimer, 1997a, 1997b) Characteristics of users for purposes of this analysis include age, gender, household income, educational attainment, and employment status. This chapter also explores the extent to which Atlantic Canadians have taken formal or informal courses or training programs related to information technology between 1993 and 1998.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".