Agricultura, comercio exterior y cooperación internacional = Agriculture, external trade and international co-operation
Bibliographic record
Abstract
CARIBBEAN 1. The agricultural sector and the economies of the regionThe loss of importance of agriculture, in comparative terms, as a source of production, employment and, frequently, foreign currency, is an almost universal concomitant of economic growth.Some of this loss of relative importance is attributable to the growing tendency to transfer activities which used to be carried out in the agricultural sphere.These activities are moved backwards (to the sector providing inputs and means of production) and forward (to the processing industry).However, the faster growth of manufacturing and services is the major factor contributing to this trend.Latin America and the Caribbean have not been exempt from these processes.During the past quarter of a century, the region has experienced a rapid decline in the share of agriculture in the gross domestic product (GDP) -from 18% to around 11%-and in employment -from close to 58% to under 30%.It must, however, be borne in mind that, unlike what happened in the case of the structural transformation processes of the developed countries and some of the recently industrialized countries, a not inconsiderable part of this relative loss of importance is due to the transfer of labour from agriculture to activities of minor economic significance (microtrade, a number of low productivity personal services and similar occupations).Although these phenomena occurred in each and every country in the region, the average results obtained are based on a widely differentiated range of national situations.As shown in figure 1, the results for 1985 constitute a real continuum -from situations in which the share of agriculture in GDP and the economically active population (EAP) is low and very low (six countries) to situations in which its share in both these variables is very high, as in Haiti and, to a lesser extent in Paraguay.It should also be noted that between 1960 and 1985 all the countries moved towards positions of a smaller relative share by agriculture in both GDP and SAP (in the chart these appear as movements leftwards and ctownwards, respectively).In the majority of the cases, an increase may also be observed in the quotient of the percentage share in GDP and the percentage share in EAP.V This suggests a relative improvement in the productivity of the agricultural labour force by comparison with the other sectors taken into account.1 KM 1 1 T 1 1 1 .i. 1 1 1 1 T i i i a. PR i hU 20 high GDP agric/GDP total (percentage) -very high 1 EAP agric/EAP total (percentage) 90
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".