Methodological and Epistemological Issues in the Interpretation of Infant Cognitive Development
Bibliographic record
Abstract
This commentary on J. Kagan (2008) addresses 2 issues. The first concerns the importance of studying developmental sequences and processes of change. The second concerns epistemological differences between contemporary neonativist approaches and classical theories of development. The commentary argues that classical theories of infant cognition and contemporary neonativist theories fundamentally differ in terms of how they conceptualize the workings of the mind. These differences affect the meaning of terms such as representation and reasoning. Also discussed are implications of epistemological differences for developmental explanations. The commentary concludes that epistemological differences must be articulated more fully in order to improve understanding of different theories and in order to evaluate the relative merits and shortcomings of those theories.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.039 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 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".