Understanding the intention of Chinese parents to enroll their children in early enrichment programs – A social media perspective
Bibliographic record
Abstract
Early learning centers are springing up rapidly in China. However, parents are confronted with high prices and difficulty in assessing the effectiveness of early enrichment programs (EEPs). This study was driven by this phenomenon and aimed to investigate parental decision-making processes of buying EEPs from a social media perspective. We sampled 271 Chinese parents and analyzed data by partial least squares based structural equation modeling (PLS-SEM). The results show that social media use intensity positively predicted injunctive norms and descriptive norms. The mediating analysis confirms that injunctive norms and descriptive norms are internalized into parental attitude to EEPs with varying degrees. This evidence serves as a new interpretation of the contrasting effect sizes of injunctive norms vs. descriptive norms on parental intention. The data however does not confirm that better-educated parents are less influenced by social norms in EEPs decision-making. Theoretical and practical implications are discussed in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".