Outlining the Language Policy and Planning (LPP) in Fiji; Taking Directions From Fiji Islands Education Commission Report of 2000
Bibliographic record
Abstract
Language is something many individuals take for granted. It is usually when we discover that our language (or language variety) is different from and perhaps less valued than, the language of others or that our options are somehow limited, either because ‘we do not speak/understand a language or language variety, or use it inappropriately or ineffectively in a particular context that we begin to pay attention to language’. This paper gives a sketch of Language Policy and Planning (LPP) which is becoming a well-researched field for many academics as well as postgraduate students. The article provides the latest pertinent information on Fiji’s LPP, the linguistic background as well as the medium of instruction (MOI). It further deliberates on the recommendations from the Fiji Islands Education Commission Report of 2000 which is a well-articulated document that provides an overt grounding for LPP in the South Pacific.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".