Bibliographic record
Abstract
The paper addresses three questions: (1) What are the most successful techniques for measuring the effects of early childhood development (ECD) programs? (2) What are likely trends of future evaluations? and (3) How can future trends take into account recent research in developmental psychology? Predictions are made on the basis of trends in ECD programs and evaluation, and advances in developmental psychology. Table 1 lists a dozen trends in early-childhood education programs which have emerged in the last quarter of the 20th century, while-Table 7 en,sions-characteristics of evaluation in the year 2010. The discussion. of tradd 1n-evaluation research, which is supplemented with illustrative data tables, cites four exemplars of different types of research, each of which is a prototype of design. It is maintained that research will vary in scale and be increasingly experimental and rigorous in design. Large scale designs will employ increasingly sensitive measures and follow up greater amounts of time. A section of the paper which outlines trends in developmental psychology notes dissatisfaction with Piagetian theory, itemizes the legacy of Piaget, and asserts that more must be known about the relationship between the child's Intellect, emotions,-and social world. An illustration of a transactional theory of development is given which serves to indicate the ways in which ECD programs can change children's thoughts and feelings through a cycle of causes and effects. (RH) Reproductions supplied by EDRS are the best that can be made from the original document.
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 | 0.332 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".