Meritocracy in the Educational System
Bibliographic record
Abstract
A meritocratic education system, by nature, is one where students are enabled to accomplish achievements, and receive corresponding rewards, regardless of outside factors. The common norm in schools is that achievement based on merit explains school success, and that merit is the only means of the upward mobility of all students in regards to societal status, regardless of age, gender, ethnicity, current social status, etc. The primary motive of this study was to determine whether education reflected this meritocratic nature and if education is merely a scale of academic achievement by examining trends within students. The materials we used to justify our results were demographic trends, school performance (self-assessment scale), and family background. Data was collected through surveys distributed to students (n = 351) with a mean age of 16.2. Our study was run within three main regions: United States, Canada, and Nigeria, and the results indicated that even though there is evidence of a correlation of a meritocratic nature in the education system (from the contingency tables), it fails to take into account socioeconomic factors, with other external factors affecting student achievement such as the generational cycle. Factors of constraint that are evident in our study include an uneven bell curve based on the categories of students surveyed, inequitable (biased) self-assessment responses, and achievement gaps in the education system.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".