Collaborative Play as New Methodology: Co-constructing Knowledge of Early Child Development in the CHILD Project in British Columbia, Canada
Bibliographic record
Abstract
The Consortium for Health, Intervention, Learning and Development (CHILD) Project was a longitudinal program of research on early child development based in British Columbia (BC), Canada, from 2003 to 2008. It drew together a wide range of university-based biomedical and social scientists, community-based professionals, and government officials and policymakers. During the five years of data collection and the subsequent period of data analysis and interpreta-tion, the CHILD Project examined the interaction and impacts of biological and social influences on early child development. Some of the studies focused on marginalized or disadvantaged popula-tions and others focused more on universal programs of research that considered the needs of all children. As part of this program of research, the CHILD Project examined the benefits and challenges of interdisciplinary inquiry by weaving together the ontologies, epistemologies, and methodologies of different disciplines (Shonkoff, 2000) and by including community professionals as research partners in the ten collaborative research studies under the CHILD umbrella. In this way, the CHILD Project became a community of discourse of community-based professionals, university researchers, and graduate students in order to ensure that the research questions, methods, and outcomes of the ten studies were relevant to children and families. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".