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README.html

@@ -687,7 +687,7 @@ <h1>Geospatial Assessment of Women Employment and Business Opportunities in the
 <span id="id4"></span><h2>4. Gender Enabling Environments Tool (GEEST)<a class="headerlink" href="#gender-enabling-environments-tool-geest" title="Permalink to this heading">#</a></h2>
 <p>Based on the Methodological Framework, the GEEST, an open-source plugin in QGIS, was developed for the automatic computation of the factors and dimensions. The GEEST characterizes communities based on women’s prospects to secure jobs or establish their own businesses within the RE sector. It aims to assist decision-makers in selecting optimal locations for RE projects, ensuring the maximum positive impact on communities and addressing gender disparities. Additionally, it provides insights for building the necessary infrastructure around RE projects to create enabling environments that enhance women’s participation in the RE sector in SIDS.</p>
 <p>The table below outlines the dimensions, factors, and recommended indicators for computing the GEEST, derived from the Methodological Framework:</p>
-<p>The GEEST generates raw score outputs for 21 of the 23 factors outlined in the Analytical Framework. The factors (i) Water and Sanitation and (ii) Fragility, Conflict, and Violence are absent due to lack of data availability. Each of the 21 factors, dimensions, and overall aggregate scores are assessed on a scale ranging from 0 to 5.</p>
+<p>The GEEST generates raw score outputs for 15 factors outlined in the Analytical Framework. Each of the 15 factors, dimensions, and overall aggregate scores are assessed on a scale ranging from 0 to 5.</p>
 <p>The interpretation of these scores is thoroughly detailed in the Methodology Report: <a class="reference external" href="https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099121123091527675/p1792120dc820d04409928040a279022b42">https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099121123091527675/p1792120dc820d04409928040a279022b42</a>. Higher scores signify a more conducive environment for women to access job opportunities. Conversely, scores of 0 indicate a lack of supportive conditions for women to access employment opportunities. To enhance comprehension, the methodology further categorizes these scores into distinct ‘classes,’ offering a simplified approach to their interpretation, as shown in the following table:</p>
 <div class="pst-scrollable-table-container"><table class="table" id="class-scores-table">
 <caption><span class="caption-number">Table 1 </span><span class="caption-text">Proposed discrete score classes to enable simpler visual interpretation of raw score outputs and enable intersection with other layers of information (reproduced from the Methodology Report).</span><a class="headerlink" href="#class-scores-table" title="Permalink to this table">#</a></caption>

_sources/README.md

@@ -159,7 +159,7 @@ The table below outlines the dimensions, factors, and recommended indicators for
 Dimensions, Factors and indicators included in the Analytical Framework
 :::
 
-The GEEST generates raw score outputs for 21 of the 23 factors outlined in the Analytical Framework. The factors (i) Water and Sanitation and (ii) Fragility, Conflict, and Violence are absent due to lack of data availability. Each of the 21 factors, dimensions, and overall aggregate scores are assessed on a scale ranging from 0 to 5.
+The GEEST generates raw score outputs for 15 factors outlined in the Analytical Framework. Each of the 15 factors, dimensions, and overall aggregate scores are assessed on a scale ranging from 0 to 5.
 
 The interpretation of these scores is thoroughly detailed in the Methodology Report: <https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099121123091527675/p1792120dc820d04409928040a279022b42>. Higher scores signify a more conducive environment for women to access job opportunities. Conversely, scores of 0 indicate a lack of supportive conditions for women to access employment opportunities. To enhance comprehension, the methodology further categorizes these scores into distinct 'classes,' offering a simplified approach to their interpretation, as shown in the following table: