Bibliographic record
Abstract
Notwithstanding, more than a decade of concerted efforts towards liberalizing the economy and attracting investment towards the productive sectors of the economy and resulting annual growth rate of over 5 per cent Lao PDR, even today, exhibits symptoms of an underdeveloped economy. Over 80 per cent of its population lives in rural areas depending on agriculture and allied activities. Yet, they contribute only about 50 per cent of the GDP pointing towards low levels of productivity in the primary sector. The industrial sector, which contributes about 23 per cent of the GDP, essentially, comprises primary processing and is characterized by high regional concentration. In 2001, Vientiane Municipality and Vientiane Province together accounted for 55 per cent of the large, 41 per cent of the medium and 19 per cent of the small industrial units 1 and over 41 per cent of the industrial employment in the country. The service sector, which contributed almost a quarter of the GDP in 2001, was found dominated by wholesale and retail trade. This however, does not imply that there has not been any structural change in the economy. The observed change during the last decade mainly was in terms of a decline in the share of agriculture from about 61 per cent in 1990, 2 the initial year for which data is available, to a little over 51 per cent in 2002. Correspondingly. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".