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Record W1487627501 · doi:10.1044/2015_aja-14-0086

SoundSpace Online: The Development of an Online Resource About Hearing Loss

2015· article· en· W1487627501 on OpenAlexaff
Zheng Yen Ng, Sue Archbold, Connie Mayer, Imran Mulla

Bibliographic record

VenueAmerican Journal of Audiology · 2015
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsYork University
Fundersnot available
KeywordsAudiologyHearing lossResource (disambiguation)American Speech-Language-Hearing AssociationPsychologyMedicineComputer scienceLinguistics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.379
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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