The development of science and technology indicators in the Caribbean

Includes bibliography

Indicadores del desarrollo económico y social en América Latina, 1976 = Indicators of economic and social development in Latin America, 1976

Incluye Bibliografía

Types of discrepancy in Millennium Development Goal indicator values and measures for statistical reconciliation: Overall framework and implementation in selected thematic areas and indicators

Includes bibliography.

Identifying indicators of sustainable development using the global sustainability quadrant approach

Advances in information technology and global data availability have opened the door for assessments of sustainable development at a truly macro scale. It is now fairly easy to conduct a study of sustainability using the entire planet as the unit of analysis; this is precisely what this work set out to accomplish. The study began by examining some of the best known composite indicator frameworks developed to measure sustainability at the country level today. Most of these were found to value human development factors and a clean local environment, but to gravely overlook consumption of (remote) resources in relation to nature’s capacity to renew them, a basic requirement for a sustainable state. Thus, a new measuring standard is proposed, based on the Global Sustainability Quadrant approach. In a two‐dimensional plot of nations’ Human Development Index (HDI) vs. their Ecological Footprint (EF) per capita, the Sustainability Quadrant is defined by the area where both dimensions satisfy the minimum conditions of sustainable development: an HDI score above 0.8 (considered ‘high’ human development), and an EF below the fair Earth‐share of 2.063 global hectares per person. After developing methods to identify those countries that are closest to the Quadrant in the present‐day and, most importantly, those that are moving towards it over time, the study tackled the question: what indicators of performance set these countries apart? To answer this, an analysis of raw data, covering a wide array of environmental, social, economic, and governance performance metrics, was undertaken. The analysis used country rank lists for each individual metric and compared them, using the Pearson Product Moment Correlation function, to the rank lists generated by the proximity/movement relative to the Quadrant measuring methods. The analysis yielded a list of metrics which are, with a high degree of statistical significance, associated with proximity to – and movement towards – the Quadrant; most notably: Favorable for sustainable development: use of contraception, high life expectancy, high literacy rate, and urbanization. Unfavorable for sustainable development: high GDP per capita, high language diversity, high energy consumption, and high meat consumption. A momentary gain, but a burden in the long‐run: high carbon footprint and debt. These results could serve as a solid stepping stone for the development of more reliable composite index frameworks for assessing countries’ sustainability.

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^