Role and Scope Coverage of Speech-Related Professionals Linked to Neuro-Advancements within the Academic Literature and Canadian Newspapers
Bibliographic record
Abstract
Speech-related professionals such as speech language pathologists (SLPs) and audiologists make use of neuro-advancements including neurotechnologies such as cochlear implants (CIs), brain-computer interfaces, and deep brain stimulation. Speech-related professionals could occupy many roles in relation to their interaction with neuro-advancements reflecting the roles expected of them by their professional organizations. These roles include: service provider, promoter of neuro-products such as CIs, educator of others, neuro-related knowledge producer and researcher, advocates for their fields and their clients in relation to neuro-advancements, and influencers of neuro-policy, neuroethics and neuro-governance discussions. Lifelong learning, also known as professional development, is used as a mechanism to keep professionals up to date on knowledge needed to perform their work and could be used to support the fulfillment of all the roles in relation to neuro-advancements. Using 300 English language Canadian newspapers and academic articles from SCOPUS and the 70 databases of EBSCO-Host as sources, we found that the neuro-advancement content linked to speech-related professionals centered around CIs and brain computer interfaces, with other neuro-technologies being mentioned much less. Speech-related professionals were mostly mentioned in roles linked to clinical service provision, but rarely to not at all in other roles such as advocate, researcher or influencer of neuroethics and neuro-governance discussions. Furthermore, lifelong learning was not engaged with as a topic. The findings suggest that the mentioning of and engagement with roles of speech-related professionals linked to neuro-advancements falls short given the expectations of roles of speech-related professionals for example. We submit that these findings have implications for the education of speech-related professionals, how others perceive the role and identity of speech-related professionals, and how speech-related professionals perceive their own role.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.062 | 0.103 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".