Bibliographic record
Abstract
I am a linguist in a linguistics department rather than a clinician in an accredited program, and thus some aspects of a typical intro speech and hearing science course are well beyond my capabilities and experience, e.g., clinical decision making, intervention, etc. My approach to teaching communication disorders to linguistics students has always been to focus on discussing the communication breakdown in relation to standard linguistic insights of a typical speaker/hearer, things like natural classes and markedness, content versus grammatical function, and so on. I ask my students to understand, for instance, what is going on when a child learns phonology or an adult brain processes language; what seems to be ‘breaking’ when a child presents a speech sound disorder or an adult presents aphasia; and how a “standard” linguistic view handle (or not) the relationship of these cases. While there are many outstanding textbooks intended to introduce the field of communication disorders to students entering the field, there are few suitable for a course from “my perspective“ aimed to linguistics students. Some years ago, I realized that I was probably going to have to write (or edit) one myself. In this presentation, I present my plans for the content and organization for this textbook.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".