Bibliographic record
Abstract
By Ellyn Bernard, CSD Advisor: Dawn Girten Presentation ID: AM_C16 Abstract: The purpose of this research study is to help gather significant articles and studies to create a clearer guideline for parents and health professionals on the use of pacifiers in infants and children. It is an important topic because the prevalence of pacifier use is widespread in the United States and around the world. A Canadian study reported that up to 84% of infants use a nonnutritive device. (Ponti, 2003) The research focuses on the effect of pacifiers on speech and language production. This includes side effects from oral dentition malocclusion, otitis media and thus overall hindrance of phoneme production. The frequency and duration of the use of the nonnutritive device plays a large factor in the risks associated with its use. (Nelson, 2012) I will describe the risks and recommended usage of pacifiers or nonnutritive devices. Nelson, A. M. (2012). A Comprehensive Review of Evidence and Current Recommendations Related to Pacifier Usage. Journal of Pediatric Nursing,27(6), 690-699. doi:10.1016/j.pedn.2012.01.004 Ponti, M. (2003). Recommendations for the use of pacifiers. Paediatrics & Child Health,8(8), 515-519. doi:10.1093/pch/8.8.515
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.015 |
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".