Bibliographic record
Abstract
The last decades have witnessed a number of lapses and contradictions in the outcomes of policy and governance. It is no coincidence there has been an increasing interest from both within and without the academe in alternate systems of selection, representation and accountability, and in revisiting fairness, equity, and social mobility. This article engages a set of beliefs seen as fundamental in the debates and critiques of social systems in general, and of equality of opportunity and outcomes in particular. We thus seek to explore the factors influencing the individual perceptions of merit as opposed to chance as the determinant of success. The focus is on China, a sui generis state with a millennium-long Confucian tradition that continues to influence its meritocratic approach to education and governance. The results indicate a significant departure from the theorized explanations established in Western studies. Notably, we find that higher levels of education are negatively related with the endorsement of meritocracy, or views that effort rather than luck determines individual outcomes. At once, as we study the outlooks of Chinese citizens, we respond to and complement the emerging research with a potential to extend our conceptions of meritocracy in general.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".