Bibliographic record
Abstract
The rise of a global Internet poses a diversity of opportunities and challenges for Canadians. Predictably it also elicits rhetorical excess: that the Net will annihilate the barriers of geography and historical circumstance, or that the Net’s borderless architecture manifestly plays into the hands of global actors; that the messaging capabilities of new media will make the Internet a seamless part of enhanced social life, or that it marks the rise of new forms of deception and social alienation; that the Web supports new forms of civic engagement allowing us to talk with anyone anywhere, or that it will privatize the public sphere, removing us further from our traditions of public participation. Such rhetorical dualisms, Steven Woolgar (1999) says, help to explain why as consumers and users we are so frequently confused and disappointed by sociotechnical change. Canadian communication researchers and educators should welcome the new communicational environment and the opportunity to play a role in its public understanding and further development. Recent data collected through a variety of national and international research projects is giving us a better sense of the trend lines in Internet development. We are beginning to get some purchase on the forces at work in shaping the Internet, the players and the backstage machinery, and how these compare and contrast across geographical and historical circumstances. A growing body of evidence on public concerns gives us some sense of what might undermine the Internet’s broad base of support. Finally, there are provisional optics for looking at some of the content characteristics of the Internet, data that profile actual user activity indicate to some extent the scope and scale of content flows and audiences. Reflective practitioners are beginning to make us aware that research on human subjects has a complex ethical relationship to the virtual, especially as case-based studies and participant inquiries give us some feel for how the Net looks from the inside out. Two complementary types of data bookend current quantitative research, on which the present meta-analysis is based: media use surveys, specifically focused on the Internet, and Internet audience research, which generally produces ranking scales for the popularity of content categories and site sources. These research
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.045 | 0.012 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".