Maturation Effect or Generation Effect: Empirical Evidence from China
Bibliographic record
Abstract
The failure to separate the effects of maturation, generation and zeitgeist on culture has been hindered the identification of cultural change for years. Hofstede regards it as a confusing matter. In this paper, we firstly use a new approach to assess the three effects,.then, according to the data from the investigation of China Telecom Company, we analyze the correlation between age variable and cultural dimensions. The empirical result shows that masculinity has the most significant change in China during the past twenty years. With the aid of the above approach, we also confirmed that the generation effect does work rather than the maturation effect does. Key words: Cultural change, maturation effect, generation effect, China Resume : L’insucces de separer les effets de maturation , de generation et de l’esprit de l’epoque sur la culture a pour consequence le retard d’identification de changement culturel depuis des annees . Hofstede la considere comme une question confuse . Dans ce texte , d’abord on se sert d’une nouvelle approche pour evaluer ces trois effets pour passer ensuite a l’analyse la correlation entre l’âge variant et les dimensions culturelles selon les donnees recueillies dans les enquetes de la Compagie Telecom de Chine . Le resultat empirique montre que la masculinite a connu le changement le plus signficatif durant les deux dernieres decennies . A l’aide de cette approche ci-dessus , on affirme egalement que l’effet de generation joue son role , mais non pour l’effet de maturation . Mots-cles: changement culturel, effet de maturation, effet de generation, Chine
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".