State of the science on implementation research in early child development and future directions
Bibliographic record
Abstract
We summarize the state of the field of implementation research and practice for early child development and propose recommendations. First, conclusions are drawn regarding what is generally known about the implementation of early childhood development programs, based on papers and discussions leading to a published series on the topic. Second, recommendations for short-term activities emphasize the use of newly published guidelines for reporting data collection methods and results for implementation processes; knowledge of the guidelines and a menu of measures allows for planning ahead. Additional recommendations include careful documentation of early-stage implementation, such as adapting a program to a different context and assessing feasibility, as well as the process of sustaining and scaling up a program. Using existing implementation information by building on and improving past programs and translating them into policy are recommended. Longer term goals are to identify implementation characteristics of effective programs and determinants of these characteristics.
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 | 0.491 | 0.521 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.034 | 0.057 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.027 | 0.032 |
| Insufficient payload (model declined to judge) | 0.018 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".