Search and the City: Comparing the Use of WiFi in New York, Budapest and Montreal
Bibliographic record
Abstract
Over the past five years, the use of mobile and wireless technology in public spaces of cities around the country has grown exponentially. There has been little analysis of the ways in which the use of the wireless Internet via WiFi may differ from that of the wireline Internet. This paper compares the results from a six-month survey of the use of WiFi hotspots in New York, Budapest and Montreal. It is hoped that further analysis of these survey results will contribute to a more acute understanding of the ways in which the user patterns of particular modes of Internet access may differ internationally. The major research questions addressed in this paper are: 1) How is WiFi being used in public spaces, by whom, where, for what purposes?; 2) How does the use of WiFi differ from other communication technology?; and, 3) How is the use of WiFi similar or different across cities internationally? This paper makes the following arguments based on the survey data: first, WiFi is an important factor in attracting people to specific locations; second, the use of WiFi highly localized in that it is often used to search for information relevant to oneÕs geographic location; third, there are significant differences in the way that WiFi is used across a variety of locations including cafes, parks and other public spaces; fourth, at present, WiFi users are, for the most part, young, male and highly educated displaying the characteristics of early adopters of technology; and, fifth, there is a convergence in the ways in which WiFi is used internationally in some respects, however there are also important differences in the reasons for these uses as well divergence in other respects.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".