Biasly Region Cooperative

Bonisiwe Shabane
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biasly region cooperative

We founded Biasly in 2017 because we saw an increasing need to bring clarity to media biases found in political news. In the polarized sphere of media narratives, we feel the challenges people are facing when consuming political content and engaging in political discourse. We also recognize that in an increasingly screen-centric world, teaching media literacy will be crucial to educating and empowering new generations against political biases. With research showing that Americans do not trust 44% of the news they consumed, Biasly’s work has never been more important. Finding truly objective, factual political news is not easy and often impossible. What’s possible is identifying biases in the news and offering the full picture through stories across the political spectrum to help people make informed choices about issues that matter to them.

At Biasly, we have developed an industry-leading Bias Meter which scans hundreds of articles every day to determine political bias that uses artificial intelligence. The Bias Meter uses a complex algorithm that is trained to identify certain components of an article such as tone and language. As well as not having the inherent biases of a human being, the algorithm in our Bias Meter is always learning and improving as we continually supply it with new data. This technology is rigorous in delivering a true and accurate reading of bias in the news you read. To bolster our A.I. ratings, we also have a talented and diverse team of analysts from across the political spectrum who are dedicated to finding biases in the news using Biasly’s analyst rating methodology.

Within the rating methodology, there are seven metrics to calculate a bias score between -100 and 100 with negative numbers being liberal, zero being neutral, and positive numbers being conservative. Each article is rated by three team members including one left-leaning, one moderate, and one conservative-leaning analyst. These scores are then averaged into one composite bias score for each article. At Biasly, we do not tolerate bias in our work as we hold the same standard when analyzing our works just like when we analyze other news sources. We have no agenda regarding which stories and where we place them on our Home Page. We also display a variety of articles from multiple perspectives and randomly rotate different news sources to provide a fair representation of the story at all times.

In today’s hyper-connected digital age, news consumers are bombarded with information from thousands of sources across the political spectrum. With this explosion in content, determining the objectivity, accuracy, and reliability of news outlets has become a critical challenge. This is where Biasly steps in as a game-changing platform—offering comprehensive, data-driven insights into media bias, factual accuracy, and political leanings. Biasly is more than just a media watchdog. It leverages AI and human analysis to score articles and news sources based on a range of bias metrics. Whether you’re a casual reader or a policy analyst, Biasly helps users understand the perspective behind the news they consume and how it might shape their opinions.

Biasly utilizes proprietary machine learning algorithms alongside expert human reviewers to analyze thousands of media articles daily. Each piece of content is evaluated across multiple parameters, including: This multifaceted approach ensures that each article receives a bias score and reliability rating, allowing readers to see beyond surface-level narratives. The technology goes further by comparing the article’s stance with known political positions and public opinion to highlight where an article diverges from objective reporting. Bias in the media can subtly shape public perception, often without readers realizing it. Words are chosen, facts are emphasized or omitted, and headlines are crafted—all to support a particular narrative.

When this becomes a norm, public discourse polarizes, critical thinking diminishes, and echo chambers flourish. Use our Bias Meter to detect and mitigate biases during your news consumption. Create your free account to see the in-depth bias analytics and more. By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates. The Media Bias Chart at Biasly offers a rating of various news agencies. Biasly is an independent news and bias rating agency and is dedicated to Fighting Fear with Facts.

How it Works Government/Non-Profit/Education News Chart Biasly’s Media Bias Chart is an interactive, two-dimensional visual tool that maps hundreds of news sources based on their political bias (x-axis) and reliability (y-axis). What sets the chart apart is its AI-powered Bias Meter, which combines machine learning with human analyst input to generate objective bias and reliability scores for each outlet. It enables users to quickly see where a source falls on the political spectrum — from Very Left to Very Right — while also evaluating the overall trustworthiness of its reporting. Biasly rates news sources using a methodology that blends artificial intelligence with human analyst review.

Each article is scanned for linguistic markers like word choice, tone, framing, headline structure, and source selection — all of which may indicate political leanings. These are then analyzed using Biasly’s proprietary algorithms to assign a bias score and reliability score. Analysts further validate AI assessments by checking for factual accuracy, source credibility, and patterns in story selection or omission. This combined process ensures the bias ratings are not only data-driven but also contextually sound. Absolutely. Educators frequently use Biasly’s Media Bias Chart as a powerful visual aid to better understand and explain the complex landscape of media bias.

It’s an ideal tool for integrating into media literacy curricula, helping students grasp how political leanings and framing influence news coverage. To support classroom and newsroom use, Biasly also offers a downloadable and printable version of the Media Bias Chart, which educators can display during lessons or distribute in workshops. In addition, Biasly’s Media Literacy Education Platform offers structured courses and resources for both students and professionals to deepen their understanding of bias and media literacy. The Politician Bias Chart at Biasly offers a rating of various politicians. Biasly is an independent news and bias rating agency and is dedicated to Fighting Fear with Facts. How it Works

Biasly’s Politician Bias Chart is an interactive visual tool that maps the political leanings of U.S. politicians across the ideological spectrum—from very left to very right. The chart uses AI analysis and human review to evaluate media portrayal, policy positions, and sentiment in coverage to help users understand each politician’s bias rating. Politicians are rated using Biasly’s proprietary Bias Meter, which evaluates a combination of their public policy stances, media portrayals, and public statements. The system leverages advanced AI algorithms alongside expert human analysts to analyze hundreds of media articles, references, and coverage patterns. This approach allows the Bias Meter to place politicians accurately on the ideological spectrum, offering insight into their political leanings.

Ratings are regularly updated to reflect shifts in behavior, media sentiment, and issue positioning. To maintain fairness, Biasly uses a hybrid model of AI and human analysts. Articles are sourced from across the political spectrum, and analysis is guided by transparent criteria that prioritize consistency, factual accuracy, and balanced language. Regular audits and algorithmic updates help ensure ongoing objectivity. If you’re a politician aiming to better understand how you’re portrayed in the media and perceived by the public, you can sign up for the Politician Bias Analytics Platform to access in-depth media sentiment... Cooperatives unleash innovation and entrepreneurship, create robust communities by meeting the economic needs of their member-owners and keeping profits local.

They fill market gaps in the economy and are a particularly resilient and flexible type of business. Cooperative members own, use, and control their business democratically and can respond flexibly to the ups and downs of the business cycle. Statistics prove the resiliency of agricultural cooperatives: more than 23 percent of U.S. farmer cooperatives have been operating for 100 years or more and 77 percent are more than 50 years old. USDA Cooperative Services provides this statistical research and more. It supports cooperatives through technical assistance, information on available USDA funding, and publications on how to start and maintain cooperative businesses.

Co-op Services also researches the legal, social, and economic aspects of cooperatives. Co-op Services partners with other Federal agencies and cooperative stakeholders through the Interagency Working Group on Cooperative Development. This initiative was authorized by the 2014 Farm Bill, which mandates that the Agriculture Secretary “chair an interagency working group to foster cooperative development and ensure coordination with federal agencies and … cooperative organizations.”... Cooperatives’ Critical Role—In rural America, agricultural cooperatives provide marketing, processing, and low-cost supplies and services to their member owners. Co-ops bring electricity to more than half of the Nation’s landmass and supply e-connectivity and telecommunications to rural residents. Cooperatives meet the needs of their member-owners in all areas of the economy, not just in the agricultural and utility space.

Cooperatives provide affordable solutions to market gaps in housing, grocery, childcare, home care (including care for veterans and individuals who are elderly or disabled), banking, mutual insurance, and other financial service industries. When business owners retire or when a business faces closure, the employees can buy the business and keep their jobs. Why are cooperatives so effective for community economic development? �ٴ�3\[�'��H����A�P� �QK[��P�T��I��D��d x� ��R�5�o�k�Q-q������:I����I�(�sf�-��Q ���N׹������t��� =����Ӹ���>�H��1��"{���Ռ�$O.U�g����Xk�� �A�W�'Չ�F��1���U�?��PN�'D[�Zt�^RB�6 ��&�?G����3�ژ�K����_�I(��~T��Iׅ�!�DНu�(}��\v��L���!� t#NP�����)�@���� !��y�|� "������jjnq�W��K[V�j�����;M{�&ea���A�PX��$7��1!1 h"'Oʓ��8��?ޠA�tI’�%&m�EH5��`ɕ ��X�-�6�$vIK�W��#�굟A��G��v�;���X���T��9��?G������/���~T����6�v�I��gT��b��H'� ?R�k�|Ғ�!<��u)�+s�H�D���! )bR���'�%*L�q³U���}%L��q�n������l����a���z�4��~ R�6\�Lp�n����w ����l�I|{ �M�h��*E�G|��ua9�A&6�����O�����YQ%��H�Q,k�ZL�JD��I�O%B���s��Ol��r>I)���a p�5�]�i�=�k�M���"���f���c�O�%������'�Ef�� �#�L�ˣA�v�D���|�J��p�� ױ@F���$�n�d����I�k�G�2 h~�n�bǑ���H�,Ay��x(;Ry�Gs�k��3,-�R���b�R��0%1,�� ;�%&��m>�N$�Opn��P�d{�v�3���%$��2[ܒS6 @�A�L����B�7�<Sz�,q$O�A�{�^|Bj�C7�Sl�@�R�.wA� ��B}wφ�۷�Mmp��j�]p� �R�2v��^�eJ]?L}��3x�7X� ���$��:4�: �H9���T4-$�*))9s_� |h��v�O5�x�<��p��%�$Gq�A)M���d�C9�M~)ˀH� [q�P<��~ �A��i<}�)p��t�Om��4DJ�^�#H�L���1�JO[\Z�01�P��F��n�xiԄ��Zð @Ԥ���%��L���9üh�a� "5NBF���ftx��Pa�i���Z�A�*!�����%Wk��8v2�)бՐZ]����� '੹ۜJ�G�-��z�N�墅�wj`꓈N��psF���<� 5����w {+��'i�O?$��뷆ǂ���H��?g��|�F�w�t���n�4��τ��\᳏Dz��7p-�R�wt�x����2C$k��{��Dx$��y�_#1ɟ�HPqs~���2}7|y�E��}Ǵ�W�۾<"�'�ϽH}��ޞt�����N�� ��$�B(b���i�����9�cN���J3~�M+�2���M ����4�$ڽ[�}��p�Dʄ%�����.!� ?�D\���4;�WAw!�=�h$�+[���G�(V���G������ Ά�T�����$�S���|7� �N�e�  �� ���ʝ��`��]a.k�0��*Wv�y���}�I�J|�(�`k��9쒘:I�?P�t��R���!���Hd�I&�]$��R���tP� ��{�!�r{Ց�<��'�V�\9�'�D�s��� I��[�.��Qq�DG7�%�L�);�̈́��? ��E���� Vl<>... )F��'��>c�#2t� Mȩ��a��� ǰ?Q��Pp-0t#�c��ҫ�����D��� T�;L C��n@������^�4��4�����.�u�J����{K�F�|ZZ ����()r>����*@7���~e��颐"O�� )�{v��uI���ω��v���H3��Ұ�KKO3�JF� �8x�&���6Kf< O�"��֡�B�x���� ����= �'O�AI�rIo�������k�&IK�J�� ��/:a�L�Ħ��R�C�00e%3lK�Ԥd{���M1�pDx�RR���TH#�&�@�syq�:�J`��~��G��)�S'@9L"a1G�)Z|�$�;\ pTý�< (G>]�i-3�j��I.�)�"H�8II�@�TL�y `���th���$�����I����P� &�IL�Ј�ITKt��)c�2��]�ILD�{'?I)�gT����))r$5&��I�#[���48�>%3s�{5�i(@ĺ`�k�Pt!;�buIJk���'����Hp=���I���Jg[�Aݯ�)�8��B"�@{y�JJ��$�#T0��`��� ��iJJf��D�&!�С�u�YNH쒙�>i�S������N�*s��7�T�L���S��w�5_�O�@�Lw�+h:CO�I ������LğȀG������zh���)I����1�E�a�6cE9�Ә�����wӧd�uN��q��7�;B|~�'���[߸�[�|uIL�5��1'����`��>J����*E�pO )R�I� ʉ����~dG��!=���Tɍ��@�3��F��$3n�4���i��5��'�S���h�Ɖ%��ϔB�GR �9��H'P������$�.�% � %A���\@'H�� �"�gP�G�oH���%ZAxx����\HID��N��2�@y).�` CGy��̵�t�CiFk]h-$5��K��Ԕ� [O�7����8�����) ' �D�8W�)��ڕ� %��F��'����$�1(���'S*p1��� ��JN�ZƆ�~R<��Byh��0��gS� %��<4��O <���~I���J[�"�Ə�%2��DG�C{��A��ęt�i�k:$�O2$�"�&D�X�1�~�E �b�5�䉎��c����w�)Tu�()��O$���RA.bd�EK�T�9,$�IK;t �9PI$����...

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