States And State Transitions – Dilemmas For Dialogue
Bibliographic record
Abstract
AimsThe state subsystem is difficult to understand as a NIDCAP Professional-in-Training.NIDCAP Professionals-in-Training are expected to reliably recognize the six Brazelton states 1 , and the division of each state into the diffuse (A) or robust (B) subcategory.2,3 The AA State is a unique feature of NIDCAP naturalistic observation, recognized as a respiratory pause greater than eight seconds and defined in the NIDCAP Training Manual as 'removal from the state continuum' .3 This definition seems to contradict Prechtl's definition of the state as a "discrete mode of neurological activity, during which a group of physiologic and behavioural characteristics that regularly recur together".4 This implies that the six states are discrete; whereas the AA State definition postulates a state continuum.The complexity of states increases when referring to the APIB Manual, where States 1AA and 2AA are mentioned as "states in which severe diffuseness is embedded".5 The confusion about states, particularly the AA state, is a frequent topic of uncertainty at NIDCAP training days at our NIDCAP Training Centre.A lively discussion usually follows but without a conclusion.To gain a deeper understanding of, and to clarify uncertainties about infant state, a survey was sent to NIDCAP Trainers.Trainers were asked for their interpretation of the states seen on a short video, and for their understanding of states 1AA and 2AA.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".