Environmental & Architectural Phenomenology (Winter 2013)
Bibliographic record
Abstract
To improve outcomes for children with hearing loss, early intervention professionals must work with families to optimize children's hearing device use and the linguistic and auditory features of children's environments. Two technologies with potential use in monitoring these domains are data logging and Language Environment Analysis (LENA) technology. This study, which surveyed early intervention providers, had two objectives: (a) to determine whether providers' experiences, perspectives, and current practices indicated there was a need for tools to better monitor these domains, and (b) to gain a better understanding of providers' experiences with and perspectives on use of the two technologies. Most providers reported that they used informal, subjective methods to monitor functioning in the two domains and felt confident that their methods allowed them to know how consistently children on their caseloads were wearing their hearing devices and what their environments were like between intervention visits. Although most providers reported limited personal experience with accessing data logging information and with LENA technology, many reported receiving data logging information from children's audiologists. Providers generally believed access to the technologies could be beneficial, but only if coupled with proper funding for the technology, appropriate training, and supportive administrative policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".