The education challenges facing small nation states in the increasingly competitive global economy of the twenty‐first century
Bibliographic record
Abstract
Publication of this piece is intended as a tribute to the late Professor M. Kazim Bacchus who passed away in March 2007. The paper provides a general discussion concerning the social and educational challenges faced by small nation states in an age characterised by globalisation. The analysis first identifies some of the basic features of small states such as their population size, the nature of their economies and their impact on educational development. It is argued that these societies need to prepare their populations better to enter the increasingly competitive globalised economy of the twenty‐first century. A major challenge arises from the fact that while small states cannot do much about their size they can improve their development prospects by skilful planning. This calls for greater flexibility in the approach of small states to the development and utilisation of their own human resources. It is argued that small states need to develop in their population a high degree of flexibility through the skills and knowledge that they provide. Further the students should not have their initiative and creativity stifled through rote learning but should instead be encouraged to be enterprising, innovative and original in whatever they do and learn. These objectives should characterise any sound educational programme but they are even more important in small‐scale societies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".