SoundSpace Online: The Development of an Online Resource About Hearing Loss
Bibliographic record
Abstract
PURPOSE: The Internet has been a growing source of health information on hearing loss, but the information provided often varies in quality, readability, and usability. Additionally, the information is provided across a wide range of domains, making access challenging to those who need it. This research forum article describes the development of a new website, SoundSpace Online (Ng, Archbold, Mayer, & Mulla, 2014), which aims to tackle these issues and bring together information and resource s f or all those concerned with hearing loss. METHOD: The SoundSpace Online website’s current developmental state was reached through the following methods: (a) discussions with a group of individuals that included experts in e-learning, education, research, and hearing loss; (b) interviews with different target groups (e.g., users, families, and professionals); and (c) collaboration with contributors. RESULT: The website is structured to become a g o-to resource on various topics related to hearing loss, providing accurate, comprehensive, and functional information and resources at varying levels of complexity for the intended users. CONCLUSION: The literature and the range of interest have illustrated the need for an up-to-date website providing information and resources on hearing loss. Challenges include monitoring and keeping the website up to date; in this article, a plan of action is discussed. The website is currently in development, with plans for a launch in the near future.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".