{"id":38766,"date":"2026-02-07T21:27:08","date_gmt":"2026-02-07T20:27:08","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/sp-500-corporate-ethics-scores-11-dimensions\/"},"modified":"2026-02-07T21:27:08","modified_gmt":"2026-02-07T20:27:08","slug":"sp-500-corporate-ethics-scores-11-dimensions","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/sp-500-corporate-ethics-scores-11-dimensions\/","title":{"rendered":"S&amp;P 500 Corporate Ethics Scores &#8211; 11 Dimensions"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<h2>Dataset Overview<\/h2>\n<p>Most ESG datasets rely on corporate self-disclosures \u2014 companies grading their own homework. This dataset takes a fundamentally different approach. Every score is derived from adversarial sources that companies cannot control: court filings, regulatory fines, investigative journalism, and NGO reports.<\/p>\n<p>The dataset contains integrity scores for all S&amp;P 500 companies, scored across 11 ethical dimensions on a -100 to +100 scale, where -100 represents the worst possible conduct and +100 represents industry-leading ethical performance.<\/p>\n<h3>Fields<\/h3>\n<p>Each row represents one S&amp;P 500 company. The key fields include:<\/p>\n<ul>\n<li>\n<p>Company information: ticker symbol, company name, stock exchange, industry sector (ISIC classification)<\/p>\n<\/li>\n<li>\n<p>Overall rating: Categorical assessment (Excellent, Good, Mixed, Bad, Very Bad)<\/p>\n<\/li>\n<li>\n<p>11 dimension scores (-100 to +100):<\/p>\n<\/li>\n<li>\n<p>planet_friendly_business \u2014 emissions, pollution, environmental stewardship<\/p>\n<\/li>\n<li>\n<p>honest_fair_business \u2014 transparency, anti-corruption, fair practices<\/p>\n<\/li>\n<li>\n<p>no_war_no_weapons \u2014 arms industry involvement, conflict zone exposure<\/p>\n<\/li>\n<li>\n<p>fair_pay_worker_respect \u2014 labour rights, wages, working conditions<\/p>\n<\/li>\n<li>\n<p>better_health_for_all \u2014 public health impact, product safety<\/p>\n<\/li>\n<li>\n<p>safe_smart_tech \u2014 data privacy, AI ethics, technology safety<\/p>\n<\/li>\n<li>\n<p>kind_to_animals \u2014 animal welfare, testing practices<\/p>\n<\/li>\n<li>\n<p>respect_cultures_communities \u2014 indigenous rights, community impact<\/p>\n<\/li>\n<li>\n<p>fair_money_economic_opportunity \u2014 financial inclusion, economic equity<\/p>\n<\/li>\n<li>\n<p>fair_trade_ethical_sourcing \u2014 supply chain ethics, sourcing practices<\/p>\n<\/li>\n<li>\n<p>zero_waste_sustainable_products \u2014 circular economy, waste reduction<\/p>\n<\/li>\n<\/ul>\n<h3>What Makes This Different from Traditional ESG Data<\/h3>\n<p>Traditional ESG providers (MSCI, Sustainalytics, Morningstar) rely heavily on corporate sustainability reports \u2014 documents written by the companies themselves. This creates an inherent conflict of interest where companies with better PR departments score higher, regardless of actual conduct.<\/p>\n<p>This dataset is built using NLP analysis of 50,000+ source documents including:<\/p>\n<ul>\n<li>\n<p>Court records and legal proceedings<\/p>\n<\/li>\n<li>\n<p>Regulatory enforcement actions and fines<\/p>\n<\/li>\n<li>\n<p>Investigative journalism from local and international outlets<\/p>\n<\/li>\n<li>\n<p>Reports from NGOs, watchdogs, and advocacy organisations<\/p>\n<\/li>\n<\/ul>\n<p>The result is 11 independent scores that reflect what external evidence says about a company, not what the company says about itself.<\/p>\n<h3>Use Cases<\/h3>\n<ul>\n<li>\n<p>Alternative ESG analysis \u2014 compare these scores against traditional ESG ratings to find discrepancies<\/p>\n<\/li>\n<li>\n<p>Ethical portfolio screening \u2014 identify S&amp;P 500 holdings with poor conduct in specific dimensions<\/p>\n<\/li>\n<li>\n<p>Factor research \u2014 explore correlations between ethical conduct and financial performance<\/p>\n<\/li>\n<li>\n<p>Sector analysis \u2014 compare industries across all 11 dimensions<\/p>\n<\/li>\n<li>\n<p>ML\/NLP research \u2014 use as labelled data for corporate ethics classification tasks<\/p>\n<\/li>\n<li>\n<p>ESG score comparison \u2014 benchmark against MSCI, Sustainalytics, or Refinitiv scores<\/p>\n<\/li>\n<\/ul>\n<h3>Methodology<\/h3>\n<p>Scores are generated by Mashini Investments using AI-driven analysis of adversarial source documents.<\/p>\n<p>Each company is evaluated against detailed KPIs within each of the 11 dimensions. <\/p>\n<h3>Coverage<\/h3>\n<p>&#8211; 500 companies \u2014 S&amp;P 500 constituents <\/p>\n<p>&#8211; 11 dimensions \u2014 5,533 individual scores<\/p>\n<p>&#8211; Score range \u2014 -100 (worst) to +100 (best)<\/p>\n<p><em>CC<\/em> <em>BY-NC-SA<\/em> <em>4.0<\/em> <em>licence.<\/em><\/p>\n<p><a href=\"https:\/\/www.kaggle.com\/datasets\/mashinii\/s-and-p-500-integrity-scores-11-dimensions\">Kaggle<\/a><\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/RevolutionaryGate742\"> \/u\/RevolutionaryGate742 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1qyn350\/sp_500_corporate_ethics_scores_11_dimensions\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1qyn350\/sp_500_corporate_ethics_scores_11_dimensions\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-38766 jlk' href='javascript:void(0)' data-task='like' data-post_id='38766' data-nonce='65e0e39b87' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-38766 lc'>0<\/span><\/a><\/div><\/div> <div class='status-38766 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Dataset Overview Most ESG datasets rely on corporate self-disclosures \u2014 companies grading their own homework. This dataset&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-38766","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/38766","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=38766"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/38766\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=38766"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=38766"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=38766"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}