Meritocratic lifelong learning: responsibilisation of marginalised adults for their learning as neocolonial contract
Bibliographic record
Abstract
After the declaration of lifelong learning as the Sustainable Development Goal 4 in 2015, lifelong learning has become a new policy bandwagon. However, whether investment of time and resources needed for it should be the responsibility of marginalised adults or any other macrolevel institutions has remained elusive. Nested in the larger theoretical and scholarly debate on meritocracy, this paper analyses the World Bank’s policy documents and interviews conducted among Nepali educational policymakers. The key findings of this study suggest that under the neocolonial contract foisted by the World Bank’s policy discourses, marginalised adults are expected to take responsibility for lifelong learning and remain competitive in the global job market. They are blamed for their inability to be competitive because lifelong learning policies are guided by a fallacious discourse of meritocracy. The vision shared by Nepali policymakers shows that lifelong learning can be embedded in the sociocultural contexts of the learners rather than merely guided by meritocratic ideals.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".