Early childhood development in Africa: Interrogating constraints of prevailing knowledge bases
Bibliographic record
Abstract
The past two decades have been characterized by renewed attention to the importance of early childhood development (ECD) policies and services in the world's richest and most industrialized countries. During the same period, we have witnessed unprecedented efforts to place ECD policies on the national development planning agenda of the economically less advantaged countries of the Majority World. This paper is premised on the concern that the purposes that have led bilateral and multilateral international agencies to promote and support ECD services in Africa may also be paving the way for uncritical adoption of program and service delivery models grounded in value systems and knowledge bases that may not be appropriate for the continent. We present two critiques to highlight the dangers of ignoring the sociocultural contexts of the knowledge bases that inform ECD policies and practices. We describe one capacity-building effort, under the auspices of the Early Childhood Development Virtual University (ECDVU), to promote culturally relevant knowledge and prepare leadership personnel for Africa's emerging ECD movement. Finally, based on an exercise designed for an ECDVU cohort to engage and reflect on critiques of mainstream research and theorizing on child development, we share insights that are suggestive of the ways in which African perspectives can contribute to and enrich a global knowledge base on child development.
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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 | 0.042 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".