<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research on Felipe Vergara-Borge</title><link>https://felipevergara.com/research/</link><description>Recent content in Research on Felipe Vergara-Borge</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 04 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://felipevergara.com/research/index.xml" rel="self" type="application/rss+xml"/><item><title>Research Methods</title><link>https://felipevergara.com/research/methods/</link><pubDate>Wed, 04 Mar 2026 00:00:00 +0000</pubDate><guid>https://felipevergara.com/research/methods/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;Rigorous evaluation of adaptive gamification systems requires both spatial statistical methods and system-level stress testing. The methods described here form the core evaluation toolkit used across GAME-based research.&lt;/p&gt;
&lt;p&gt;The guiding principle is reproducibility: every metric must be computable from a fixed input log and produce identical results across runs.&lt;/p&gt;
&lt;h2 id="spatial-analysis-getis-ord-gi"&gt;Spatial Analysis: Getis-Ord Gi*&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;Getis-Ord Gi&lt;/strong&gt;* (pronounced G-i-star) is a spatial autocorrelation statistic used to identify hot spots and cold spots in geographic participation data.&lt;/p&gt;</description></item><item><title>Research Systems</title><link>https://felipevergara.com/research/systems/</link><pubDate>Wed, 04 Mar 2026 00:00:00 +0000</pubDate><guid>https://felipevergara.com/research/systems/</guid><description>&lt;h2 id="problem"&gt;Problem&lt;/h2&gt;
&lt;p&gt;Gamification research suffers from two structural problems: systems that produce non-reproducible scoring outcomes and platforms that treat all participants identically regardless of spatial or behavioral context.&lt;/p&gt;
&lt;p&gt;Standard gamification frameworks rely on heuristic scoring that varies unpredictably under concurrency. This makes it impossible to isolate the effect of a specific strategy across experiments, undermining the scientific validity of results.&lt;/p&gt;
&lt;p&gt;At the same time, static incentive structures fail to adapt to the actual participation landscape — leaving underrepresented geographic areas without targeted incentives, compounding existing data inequalities.&lt;/p&gt;</description></item></channel></rss>