A Sector Hanging in the Balance: Early Childhood Development and Lockdown in South Africa
Bibliographic record
Abstract
New evidence suggests that over four months after the closure of early childhood development (ECD) programmes on 18 March 2020, the ECD sector was likely to be operating at less than a quarter of its pre-lockdown levels. Of the 38 percent of respondents from the new NIDS-CRAM survey reporting that children aged 0-6 in their households had attended ECD programmes before the lockdown in March, only 12 percent indicated that children had returned to these programmes by mid-July, well after programmes were allowed to reopen. Using these findings, we estimate that just 13 percent of children aged 0-6 were attending ECD programmes by mid-July to mid-August compared to 47 percent in 2018. The last time that ECD attendance rates were as low as this was in the early 2000s. At this point it is not yet clear what proportion of these declines are only temporary, or whether there will be a lasting impact on ECD enrolment in the country. This dramatic contraction in the ECD sector relates to prohibitive costs to reopening ‘safely’ imposed by the regulatory environment, coupled with shocks to the demand side for ECD programmes (both in terms of reduced household incomes and parent fears of children contracting COVID-19). When viewed from a broader socio-economic lens, the threat of ECD programme closures across the nation will have impacts beyond ECD operators to the lives of millions of children, millions of households and millions of adults who rely on these ECD services. A swift intervention by government is necessary to save this important sector and limit the ripple effect of programme closures on multiple layers of society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".