Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>WiFi is the fastest and most cost-effective way of wireless Internet connectivity. Nowadays, almost all of the mobile phones and an increasing number of home entertainment systems are WiFi-enabled. Being the key enabler of the “Internet of Everything”, WiFi brings including people, processes, data and devices, together and turns data into valuable information that makes life better and business thrive. With all mobile devices, wearable gadgets, home entertainment systems and home automation systems connected together and linked to the Internet, devices can now interact with one another and data be shared among the devices. However, transmitting information across the WiFi network means leaving your computer or devices vulnerable to attack, giving unscrupulous people the opportunity to intercept traffic, selectively eavesdrop on critical communications or even the administrative access and thus the ability to harvest all the information they want. All these threats highlight the growing importance of keeping your WiFi secure from unauthorized access and malicious attacks.</p><p>Basing on empirically collected quantitative data, this paper presents a comprehensive study on Hong Kong people’s knowledge about WiFi security and their use of WiFi in connecting the Internet. Findings of the study shed light on the knowledge gaps of Hong Kong WiFi users in using and setting up WiFi connections so that service providers, policy makers and stakeholders can devise appropriate security measures to improve the security of WiFi connection. The study also canvasses and analyses the views of the users on the connectivity and quality of free and commercial WiFi service in Hong Kong. The findings can help government and private WiFi operators to further improve the service provided. </p>
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it