Human Capital, White Elephants, and Global Competitiveness: Predicament of Distorted Educational Policy Borrowing in Kazakhstan's Universities
Bibliographic record
Abstract
Global competitiveness is like the lion and the impala roaming the open field in the Serengeti looking for booty. Each freely comes and goes but with a prize for the impala to pay because the mighty lions are in control and can consume the small and weak animals at their discretion. The analogy of the lion and the impala above signifies the generally implied notion of incursions of advanced knowledge-based economies in less developed countries. This study investigates the possibilities offered by indiscriminate educational policy borrowing from advanced knowledge-based economies to reform human capital development and make Kazakhstan's universities globally competitive. The article critically examines and reformulates the beliefs about human capital and how it induces development and enhances global competitiveness. The present inquiry utilizes a qualitative methodology case study to investigate the global competitiveness goals behind educational policy borrowing in Kazakhstan. We draw a linkage between global competitiveness and educational policy borrowing. Our findings indicate that educational policy borrowing in context is a means of advancement that streams development criteria into global competitiveness. Whereas it is essential to become closer to advanced knowledge-based economies, it is also important to find ways to avoid the forfeiture of the best accomplishments of national education, self-identity, and culture.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".