School-Community Relations and Fee-Free Education Policy in Papua New Guinea
Bibliographic record
Abstract
While international and Pacific scholarship suggests that communities can play a significant role in improving access to schooling as well as school funding, infrastructure, and resources, there is little research on how school-community relations shape the implementation of fee-free education policies. This is particularly the case in the Pacific region. In Papua New Guinea, communities play a significant role in determining school funding, infrastructure, and access, but their role in implementing the country’s fee-free education policy (introduced in 2012) is poorly understood. Drawing on data from two provinces with very different capacities for service delivery, this paper shows that school-community relations vary significantly, and are crucial for managing challenges associated with the country’s tuition fee-free (TFF) policy, particularly in regards to access to schooling and improving school funding, infrastructure, and resources. While communities have helped advance the TFF policy’s goals, conflict over land, the charging of fees, and the board of management (BOM)—a key local governance body— has, in some cases, undermined national efforts to increase enrollments and make up for the loss of school income from tuition fees. This paper argues that academics and policy makers need to pay greater attention to the sustainability of fee-free education policies, geographical variation, and the improvement of school-community relations. Doing so will require overcoming the tendency to focus on national-level indicators of success associated with fee-free education policies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".