The Mobile-izing Japanese: Connecting to the Internet by PC and Webphone in Yamanashi
Bibliographic record
Abstract
This paper is the first report of a very recent study of the users and uses of the Internet in Japan. Survey data from 1320 adult respondents was collected in late 2002 in the somewhat rural prefecture of Yamanashi, not far from Tokyo. Participants were asked about their use of both mobile phones and personal computers (PCs): how often they used them, what they used them for, whom they contacted, and what was the nature of their social networks and social support. As the first paper reporting these findings, we compare the users of Internet-connected PCs and web-enabled mobile phones (which we call "webphones"). We report on the extent to which they communicate and the nature of their social networks. Our key research questions are: a. Who uses webphones and PCs? b. How do people use them? Research for this study has been supported by grants from the Japan Society for the Promotion of Science, The Matsushita International Foundation, the Social Science and Research Council of Canada and the U.S. National Science Foundation. The Centre for Urban and Community Studies, University of Toronto, happily hosted Kakuko Miyata on her research leave, 2002-2003. We thank Mitsuhiro Ura, Hiroshi Hirano, Tetsuro Kobayshi, Kaichiro Furutani, Irena Shklovski, Mimi Ito and Rachel Yould for their advice, and Vicky Boase, Monica Prijatelj, and Uyen Quach for their assistance in the final preparation of this paper. In part, our work addresses the ongoing debate about the effects of Internet use on community and alienation. We differ from other studies by: a. Focusing on the Japanese context, which is so different than the more widelyreported American context. b. Comparing communication via mobile phones and PCs. While we believe that our results are interesting in themselves, we further b...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".