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		<title>AI is already rewriting reality for billions of people. It is getting women wrong.</title>
		<link>https://mtpdculture.org/articles/ai-is-already-rewriting-reality-for-billions-of-people-it-is-getting-women-wrong/</link>
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		<pubDate>Tue, 07 Jul 2026 10:18:52 +0000</pubDate>
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					<description><![CDATA[https://unric.org/en/ai-is-already-rewriting-reality-for-billions-of-people-it-is-getting-women-wrong/ A study of 133 AI systems found that 44 per cent demonstrated gender bias and 26 per cent demonstrated both gender and racial bias. Yet only 51 per cent of marketers currently use human oversight to test AI-generated creative before release. Ahead of the United Nations Global Dialogue on Artificial Intelligence Governance from 6 [&#8230;]]]></description>
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									<p>https://unric.org/en/ai-is-already-rewriting-reality-for-billions-of-people-it-is-getting-women-wrong/</p><p>A study of 133 AI systems found that 44 per cent demonstrated gender bias and 26 per cent demonstrated both gender and racial bias. Yet only 51 per cent of marketers currently use human oversight to test AI-generated creative before release. Ahead of the United Nations Global Dialogue on Artificial Intelligence Governance from 6 – 7 July and AI for Good Global Summit in Geneva, Switzerland from 7-10 July, UN Women sets out what is at stake – and what must change – to build a gender-equal digital future.</p><p>1. The AI content era is here. And the window to shape it is closing fast.<br />Generative AI is now among the most widely used technologies in day-to-day marketing and communications work, in the United Kingdom (UK) alone, 88 per cent of advertising and media agencies are already using it in some form. Discriminatory algorithms could therefore further perpetuate gender inequality and discrimination. As AI tools become embedded in content generation and media buying at scale, decisions about who gets seen, how they are portrayed, and whose stories get told are being made at speed, and largely without human scrutiny or gender perspective.</p><p>2. Bias and discriminatory algorithms are not a glitch in AI – it is a pattern documented across systems at scale.<br />Large Language Models (LLMs) have been found to consistently associate women with “home,” “family,” and “children,” and men with “business,” “executive,” “salary,” and “career.” When tasked with completing sentences that start with a person’s gender, about 20 per cent of responses from LLMs exhibited sexist and misogynistic attitudes, including portrayals of women as sex objects and property of their husbands. These are the predictable output of AI systems trained on decades of unequal representation of women and men. AI bias is not only a system design problem, but also a policy problem. Of 138 countries assessed, only 24 referenced gender in a national AI strategy, and just 18 included substantive gender-responsive provisions, risking inequality being “baked in” to future systems.</p><p>3. AI is intensifying violence against women and girls in digital spaces.<br />According to UN Women data, women and girls globally already have less access to digital spaces – and when they do, they are far more likely to experience online violence. Almost one in four surveyed women human rights defenders, activists and journalists had experienced AI-assisted online violence and 12 per cent report having experienced the non-consensual sharing of personal images, including intimate or sexual content. Six per cent say they have been targeted through “deepfakes” or manipulated images/video, while more than one in four have received unsolicited sexual advances through digital messaging. AI is compounding this. Deepfakes are among the most visible examples of AI-enabled abuse that disproportionately targets women and girls. As AI-generated content becomes the norm, the tools for harassment, manipulation, and image-based abuse are scaling alongside it.</p><p>4. Women are being locked out of the rooms where AI is built.<br />Gen AI is expected to drive job growth in tech-intensive sectors, yet women remain underrepresented in Science, Technology, Engineering and Mathematics (STEM) and AI, making up only 30 per cent of the AI workforce globally. The people designing these systems are not representative of the billions of people the systems are expected to serve – and that glaring gap is compounding the problem.</p><p>5. The economic disruption of AI will fall hardest on women.<br />Women outside the AI sector are nearly twice as likely as men to hold jobs at high risk of automation. AI disparity does not manifest in gender inequality alone – harms are multiplied across race, disability, socioeconomic status, and geography. The communities already most underrepresented in media and labour markets face the greatest risk of being left further behind.</p><p>6. Inclusive AI is a commercial imperative.<br />In a first-ever global study, the Unstereotype Alliance, an industry-led initiative convened by UN Women, proved that inclusive advertising has a positive impact on business profit, sales and brand value. Brands that create inclusive advertising, free of gender stereotypes, enjoy +3.46 per cent short-term sales and +16.26 per cent long-term sales uplift. They are 62 per cent more likely to be a consumer’s first choice, have 54 per cent higher pricing power, and experience 15 per cent higher customer loyalty. As AI becomes central to how campaigns are planned and produced, the brands that embed inclusion into those processes stand to gain – and those that do not, face significant reputational and commercial risk. The Unstereotype Alliance playbook launched in June 2026 gives marketers a way to catch bias before it ships, every time they use generative AI.</p><p>UN Women calls for gender equality and the rights and experiences of women and girls to be embedded at every stage of AI life cycle from development, deployment, and governance. When designed with safety and used with intention, AI can help detect stereotypes, broaden representation, and improve accessibility at scale. The choice of whether it does lies with the people making decisions – in governments, in companies, in experts researching and developing AI – and it depends on whether we incorporate the voice, expertise, and lived experience of women and girls from diverse contexts, civil society organizations who work with them and know their issues deeply.</p>								</div>
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		<title>Data colonialism and indigenous languages in AI: a critical review of existing initiatives and their struggles with data sovereignty</title>
		<link>https://mtpdculture.org/articles/data-colonialism-and-indigenous-languages-in-ai-a-critical-review-of-existing-initiatives-and-their-struggles-with-data-sovereignty/</link>
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		<pubDate>Sun, 21 Jun 2026 09:28:15 +0000</pubDate>
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					<description><![CDATA[Jenny C. Y. Kwok This article critically reviews recent initiatives to employ artificial intelligence (AI), particularly large language models (LLMs), for the revitalization of Indigenous languages. Structured by geographical contexts, the analysis includes Irish Gaelic (Europe), Māori (Aotearoa/New Zealand, Oceania), Guaraní (Paraguay/Bolivia, South America), and Inuktitut (Canada, North America). Applying a theoretical framework grounded in [&#8230;]]]></description>
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<p class="wp-block-paragraph"><a href="https://philpapers.org/s/Jenny%20C.%20Y.%20Kwok" target="_blank" rel="noopener">Jenny C. Y. Kwok</a></p>



<p class="wp-block-paragraph">This article critically reviews recent initiatives to employ artificial intelligence (AI), particularly large language models (LLMs), for the revitalization of Indigenous languages. Structured by geographical contexts, the analysis includes Irish Gaelic (Europe), Māori (Aotearoa/New Zealand, Oceania), Guaraní (Paraguay/Bolivia, South America), and Inuktitut (Canada, North America). Applying a theoretical framework grounded in data colonialism and Indigenous data sovereignty, the article examines the key achievements in different regional endeavors, as well as investigate how government-led projects and Big Tech collaborations across these diverse contexts navigate (or fail to navigate) issues of data extraction, community consent, cultural representation, and ownership. Through this lens, the article identifies specific ethical pitfalls as well as commendable practices that either reproduce colonial dynamics or empower Indigenous communities. This critique emphasizes regional and contextual nuances, arguing that authentic community agency and rigorous adherence to Indigenous data sovereignty principles are vital to ensuring ethical AI practices and meaningful linguistic revitalization.</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3592</post-id>	</item>
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		<title>AI is ushering in a new era of colonialism</title>
		<link>https://mtpdculture.org/articles/ai-is-ushering-in-a-new-era-of-colonialism/</link>
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		<pubDate>Sun, 21 Jun 2026 09:05:46 +0000</pubDate>
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					<description><![CDATA[Josephine Walker: As&#160;AI&#160;changes the way the world gathers information, some critics say that it is perpetuating stereotypes and erasing cultural nuances for Indigenous groups and people of color. Most mainstream models are trained on the work of Western writers—particularly white men—and regularly mimic those values,&#160;writing styles, viewpoints, and&#160;biases. Some critics say the&#160;data grab&#160;is a new [&#8230;]]]></description>
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<p class="wp-block-paragraph"><br>Josephine Walker:</p>



<p class="wp-block-paragraph">As&nbsp;AI&nbsp;changes the way the world gathers information, some critics say that it is perpetuating stereotypes and erasing cultural nuances for Indigenous groups and people of color. Most mainstream models are trained on the work of Western writers—particularly white men—and regularly mimic those values,&nbsp;<a href="https://www.axios.com/2026/05/02/ai-changing-writing-speaking" target="_blank" rel="noopener">writing styles</a>, viewpoints, and&nbsp;<a href="https://www.axios.com/2020/07/22/artificial-intelligence-bias-gender-race-religion" target="_blank" rel="noopener">biases</a>. Some critics say the&nbsp;<a href="https://www.axios.com/technology/big-tech" target="_blank" rel="noopener">data grab</a>&nbsp;is a new form of&nbsp;<a href="https://www.axios.com/2020/10/23/european-museums-return-looted-relics" target="_blank" rel="noopener">colonialism</a>, where information gathering replaces Imperial-era land seizures while the AI companies—rather than a conquering nation—reap profits from marginalized groups. Data collection from these groups is often done without their consent or any verification that the information is accurate.</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3583</post-id>	</item>
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		<title>Tokenising culture: causes and consequences of cultural misalignment in large language models &#8211;</title>
		<link>https://mtpdculture.org/articles/tokenising-culture-causes-and-consequences-of-cultural-misalignment-in-large-language-models/</link>
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		<pubDate>Sun, 21 Jun 2026 09:02:22 +0000</pubDate>
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					<description><![CDATA[How do AI systems embed cultural values and what risks does this imply? Jorge Perez Little attention is instead given to what values LLMs may reflect and what behaviours they may assume beyond those relating to safety. How well do the values, beliefs and behaviours of these models align with those of their users?]]></description>
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<p class="wp-block-paragraph">How do AI systems embed cultural values and what risks does this imply?</p>



<p class="wp-block-paragraph"><a href="https://www.adalovelaceinstitute.org/person/jorge-perez/" target="_blank" rel="noopener">Jorge Perez</a></p>



<p class="wp-block-paragraph">Little attention is instead given to what values LLMs may reflect and what behaviours they may assume beyond those relating to safety. How well do the values, beliefs and behaviours of these models align with those of their users?</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3580</post-id>	</item>
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		<title>Communicating the cultural other: trust and bias in generative AI and large languagemodels</title>
		<link>https://mtpdculture.org/articles/communicating-the-cultural-other-trust-andbias-in-generative-ai-and-large-languagemodels/</link>
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		<pubDate>Sun, 21 Jun 2026 08:55:24 +0000</pubDate>
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					<description><![CDATA[Christopher J. Jenks: This paper is concerned with issues of trust and bias in generative AI ingeneral, and chatbots based on large language models in particular (e.g. ChatGPT).The discussion argues that intercultural communication scholars must do more tobetter understand generative AI and more specifically large language models, assuch technologies produce and circulate discourse in an [&#8230;]]]></description>
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<p class="wp-block-paragraph">Christopher J. Jenks:</p>



<p class="wp-block-paragraph">This paper is concerned with issues of trust and bias in generative AI in<br>general, and chatbots based on large language models in particular (e.g. ChatGPT).<br>The discussion argues that intercultural communication scholars must do more to<br>better understand generative AI and more specifically large language models, as<br>such technologies produce and circulate discourse in an ostensibly impartial way,<br>reinforcing the widespread assumption that machines are objective resources for<br>societies to learn about important intercultural issues, such as racism and discrim<br>ination. Consequently, there is an urgent need to understand how trust and bias<br>factor into the ways in which such technologies deal with topics and themes central<br>to intercultural communication. It is also important to scrutinize the ways in which<br>societies make use of AI and large language models to carry out important social<br>actions and practices, such as teaching and learning about historical or political<br>issues</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3577</post-id>	</item>
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		<title>Cultural Bias in Large Language Models: Evaluating AI Agents through Moral Questionnaires</title>
		<link>https://mtpdculture.org/articles/cultural-bias-in-large-language-models-evaluating-ai-agents-through-moral-questionnaires/</link>
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		<pubDate>Sun, 21 Jun 2026 08:49:35 +0000</pubDate>
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		<guid isPermaLink="false">https://mtpdculture.org/?p=3574</guid>

					<description><![CDATA[Simon Münker Are AI systems truly representing human values, or merely averaging across them? Our study suggests a concerning reality: Large Language Models (LLMs) fail to represent diverse cultural moral frameworks despite their linguistic capabilities. We expose significant gaps between AI-generated and human moral intuitions by applying the Moral Foundations Questionnaire across 19 cultural contexts. [&#8230;]]]></description>
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<p class="wp-block-paragraph"><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=M%C3%BCnker,+S" target="_blank" rel="noopener">Simon Münker</a></p>



<p class="wp-block-paragraph">Are AI systems truly representing human values, or merely averaging across them? Our study suggests a concerning reality: Large Language Models (LLMs) fail to represent diverse cultural moral frameworks despite their linguistic capabilities. We expose significant gaps between AI-generated and human moral intuitions by applying the Moral Foundations Questionnaire across 19 cultural contexts. Comparing multiple state-of-the-art LLMs&#8217; origins against human baseline data, we find these models systematically homogenize moral diversity. Surprisingly, increased model size doesn&#8217;t consistently improve cultural representation fidelity. Our findings challenge the growing use of LLMs as synthetic populations in social science research and highlight a fundamental limitation in current AI alignment approaches. Without data-driven alignment beyond prompting, these systems cannot capture the nuanced, culturally-specific moral intuitions. Our results call for more grounded alignment objectives and evaluation metrics to ensure AI systems represent diverse human values rather than flattening the moral landscape.</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3574</post-id>	</item>
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		<title>Cultural bias and cultural alignment of large language models</title>
		<link>https://mtpdculture.org/articles/cultural-bias-and-cultural-alignment-of-large-language-models/</link>
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		<pubDate>Sun, 21 Jun 2026 08:45:26 +0000</pubDate>
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					<description><![CDATA[Yan Tao , Olga Viberg , Ryan S Baker , René F Kizilcec Abstract Culture fundamentally shapes people’s reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people’s authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated [&#8230;]]]></description>
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<p class="wp-block-paragraph"><a href="javascript:;">Yan Tao</a> , <a href="javascript:;">Olga Viberg</a> , <a href="javascript:;">Ryan S Baker</a> , <a href="javascript:;">René F Kizilcec</a></p>



<h2 class="wp-block-heading" id="481508017">Abstract</h2>



<p class="wp-block-paragraph">Culture fundamentally shapes people’s reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people’s authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI’s GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models’ responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For later models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models’ output for 71–81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.</p>



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		<title>Reflective Assignments in Health Interpreter Education: Developing an Awareness of Intercultural Capabilities and Issues</title>
		<link>https://mtpdculture.org/articles/reflective-assignments-in-health-interpreter-education-developing-an-awareness-of-intercultural-capabilities-and-issues/</link>
					<comments>https://mtpdculture.org/articles/reflective-assignments-in-health-interpreter-education-developing-an-awareness-of-intercultural-capabilities-and-issues/#respond</comments>
		
		<dc:creator><![CDATA[MTPD Culture]]></dc:creator>
		<pubDate>Tue, 21 Apr 2026 11:46:11 +0000</pubDate>
				<category><![CDATA[Articles]]></category>
		<category><![CDATA[Healthcare]]></category>
		<category><![CDATA[Law]]></category>
		<guid isPermaLink="false">https://mtpdculture.org/?p=3482</guid>

					<description><![CDATA[ In Aotearoa/New Zealand, interpreters working in public service settings must hold some form of credentialling by the National Accreditation Authority for Translators and Interpreters (NAATI). The NAATI guidelines for Interpreters (2016) require that interpreters develop and demonstrate intercultural competence. Interpreters also need to identify any cultural bias, prejudices, power dynamics, and stereotypes they might hold [&#8230;]]]></description>
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<p class="wp-block-paragraph"> In Aotearoa/New Zealand, interpreters working in public service settings must hold some form of credentialling by the National Accreditation Authority for Translators and Interpreters (NAATI). The NAATI guidelines for Interpreters (2016) require that interpreters develop and demonstrate intercultural competence. Interpreters also need to identify any cultural bias, prejudices, power dynamics, and stereotypes they might hold in relation to their clients. Most importantly, interpreters are also required to demonstrate intercultural competence while maintaining impartiality to achieve pragmatic equivalence. Consequently, developing intercultural competence during interpreter education is essential. </p>



<p class="wp-block-paragraph"><a href="https://www.researchgate.net/profile/E-Ramirez-4?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19" target="_blank" rel="noopener">E. Ramirez</a><a href="https://www.researchgate.net/institution/Auckland-University-of-Technology?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19" target="_blank" rel="noopener"> Auckland University of Technology</a><a href="https://www.researchgate.net/profile/Ineke-Crezee" target="_blank" rel="noopener"></a></p>



<p class="wp-block-paragraph"><a href="https://www.researchgate.net/profile/Ineke-Crezee?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19" target="_blank" rel="noopener">Ineke Hendrika Martine Crezee</a> <a href="https://www.researchgate.net/institution/Auckland-University-of-Technology?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19" target="_blank" rel="noopener">Auckland University of Technology</a></p>



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<div class="wp-block-button"><a class="wp-block-button__link wp-element-button">https://www.researchgate.net/publication/388323580_Reflective_Assignments_in_Health_Interpreter_Education_Developing_an_Awareness_of_Intercultural_Capabilities_and_Issues</a></div>
</div>



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		<post-id xmlns="com-wordpress:feed-additions:1">3482</post-id>	</item>
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		<title>Why studying languages still matters by</title>
		<link>https://mtpdculture.org/articles/why-studying-languages-still-matters-by-dr-elba-ramirez-aut/</link>
					<comments>https://mtpdculture.org/articles/why-studying-languages-still-matters-by-dr-elba-ramirez-aut/#respond</comments>
		
		<dc:creator><![CDATA[MTPD Culture]]></dc:creator>
		<pubDate>Mon, 20 Apr 2026 08:16:16 +0000</pubDate>
				<category><![CDATA[Articles]]></category>
		<category><![CDATA[Education]]></category>
		<guid isPermaLink="false">https://mtpdculture.org/?p=3478</guid>

					<description><![CDATA[Elba RamirezSenior lecturer and programme leader for international studies at the School of Social Sciences and Public Policy (Te Wānanga Aronui o Tāmaki Makau Rau), Auckland University of Technology, New Zealand.]]></description>
										<content:encoded><![CDATA[
<div class="wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex">
<div class="wp-block-button"><a class="wp-block-button__link wp-element-button" href="https://courier.unesco.org/en/articles/why-studying-languages-still-matters" target="_blank" rel="noopener">Why studying languages still matters</a></div>
</div>



<p class="wp-block-paragraph"><strong>Elba Ramirez</strong><br>Senior lecturer and programme leader for international studies at the School of Social Sciences and Public Policy (Te Wānanga Aronui o Tāmaki Makau Rau), Auckland University of Technology, New Zealand.</p>



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		<post-id xmlns="com-wordpress:feed-additions:1">3478</post-id>	</item>
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		<title>Relationships between six cultural scales and ten ageism dimensions: Correlation analysis using data from 31 countries</title>
		<link>https://mtpdculture.org/articles/relationships-between-six-cultural-scales-and-ten-ageism-dimensions-correlation-analysis-using-data-from-31-countries/</link>
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		<dc:creator><![CDATA[MTPD Culture]]></dc:creator>
		<pubDate>Wed, 20 Aug 2025 15:31:12 +0000</pubDate>
				<category><![CDATA[Articles]]></category>
		<category><![CDATA[Healthcare]]></category>
		<guid isPermaLink="false">https://mtpdculture.org/?p=3350</guid>

					<description><![CDATA[As the aging of the world accelerates, clarifying the relationship between cultural differences and ageism is an urgent issue. Therefore, in this study, we conducted a correlation analysis between the six cultural scales of Hofstede et al. [1] and the 10 ageism scales calculated from data on 35,232 people from 31 countries included in the [&#8230;]]]></description>
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<p class="wp-block-paragraph">As the aging of the world accelerates, clarifying the relationship between cultural differences and ageism is an urgent issue. Therefore, in this study, we conducted a correlation analysis between the six cultural scales of Hofstede et al. [1] and the 10 ageism scales calculated from data on 35,232 people from 31 countries included in the World Values Survey Wave 6 by Inglehart et al. [2]. The results of a partial correlation analysis controlling for economic and demographic factors showed that the cultural scales were correlated with ageism. This is the first study to show that diverse cultural scales are related to multiple dimensions of ageism.</p>



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