Bibliographic record
Abstract
Hearables, a term first coined by Hunn (2014), are wireless, smart, micro-computers with artificial intelligence that incorporate both speakers and microphones.They fit in the ears and can connect to the Internet and to other devices; they are designed to be worn daily.These devices, such as the Bragi Dash, Vinci, and Bose Hearphone are now appearing on the market, which is expected to exceed $40 billion in the USA by 2020 (Omnicom, 2018).Hearables are not headphones, nor hearing aids, nor ear plugs, although they could take on the affordances of any of these devices (Banks, 2018).Headphones are designed for listening to music.Hearing aids are designed as an aid for the hearing impaired.Ear plugs reduce unwanted sounds by cancelling noise.Hearables offer comparable features and additionally provide users with a microphone and connectivity to the Internet, thus supporting telephony and personal digital assistant (PDA) services (Computational Thinkers, n.d.).Prior to 2017, in the USA, such devices required the approval of the Food and Drug Administration.This approval is no longer required for hearables, as they are no longer considered to be medical hearing aids (Over the Counter Hearing Aid Act, 2017).This paves the way for the expansion in the market of significantly lower-priced hearables, undercutting the expensively-priced hearing aid market.Hearables stream music or audio content wirelessly using Bluetooth.Phone calls can be taken hands-free.Noises can be filtered out and speech amplified and filtered.And, with augmented audio, hearables can transform the user experience with sound controls and special effects (Traynor, 2017) Hearables can be also used to simply enhance the listening experience; Hunn (2014) refers to them as the "new wearables."As such, they represent a subset of wearable computers, which now includes wrist bands like Fitbit, eye wear such as Google Glass, intelligent garments such as CoolShirt, or shoes such as Nike+.Hearables must be distinguished from audibles such as Amazon's Alexa, Apple's Siri, Microsoft's Cortana and Google Assistant.The difference is that of mobility -hearables can go anywhere with the user, whereas audibles are place-based.To date, hearable companies have focused on either music, because of its wide popularity, or the health
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.171 |
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".