The United States Fish and Wildlife Service in Alaska, USA, conducted a species status assessment for a petition to list the Alexander Archipelago wolf (Canis lupus ligoni) under the Endangered Species Act in 2020-2022. This federal undertaking could not be adequately prepared without including the knowledge of Indigenous People who have a deep cultural connection with the subspecies. Our objective is to communicate the authoritative expertise and voice of the Indigenous People who partnered on the project by demonstrating how their (...) knowledge contributed to the species status assessment. The Indigenous knowledge applied in the assessment is the cultural and intellectual property of those who have shared it. We employed rapid appraisal research to expeditiously develop a preliminary and qualitative understanding of Indigenous People's cultural and ecological knowledge of Alexander Archipelago wolves. We used semi-directed interviewing and inductive coding from grounded theory for text analysis. Indigenous knowledge contributed to the agency's understanding of the Alexander Archipelago wolf in Southeast Alaska and helped the agency with their classification decision. Indigenous research partners explained the rich cultural significance and position of wolves in Tlingit society and described human–wolf relationships and ecological interactions. The agency used a single-species assessment approach based in species ecology and conservation biology, whereas the Indigenous wolf experts applied a multi-species, community ecology approach based in a sociocultural context of balance and respect. The Indigenous wolf experts successfully addressed knowledge gaps identified by the agency. The partners were challenged by a short regulatory timeframe that did not allow for comprehensive study of Indigenous knowledge and constrained review and feedback by Indigenous experts. The United States Fish and Wildlife Service learned that its assessment framework was not designed to account for an Indigenous worldview. To level the playing field, the agency and Indigenous experts should discuss how to co-develop an assessment framework that equitably applies both perspectives. (shrink)

The shareholder wealth maximization objective for corporate management can be a very effective tool for decision making. However, it can also be used to rationalize the commission of unethical or socially irresponsible actions. Overemphasis on the SWM objective by some companies can lead to dangerous or disastrous consequences for consumers, employees, or the general population. Even so, issues of business ethics and social responsibility (BE-SR) are almost totally ignored in corporate finance textbooks. If the typical coverage of corporate finance courses (...) is represented by these textbooks, then financial educators are abdicating their responsibility to help prepare future corporate managers to recognize and deal with BE-SR issues effectively. (shrink)

Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all the time (Kryeziu & Shehu, 2022). Natural Language Processing is a very active field of study, where transformers have a key role. Transformers function based on neural networks and they are increasingly showing promising results. One of the first major contributions to transfer learning in Natural Language Processing was the use of pre-trained word embeddings in (...) 2010 (Joseph, Lev, & Yoshua, 2010). Pre-trained models like ELMo (Matthew, et al., 2018) and BERT (Delvin, et al., 2019) are trained on large corpora of unlabeled text and as a result learning from text representations has achieved good performance on many of the underlying tasks on datasets from different domains. Pre-training in the language model has proven that there has been an improvement in some aspects of natural language processing, based on the paper (Dai & Le, 2015). In present paper, we will pre-train BERT on the task of Masked Language Modeling (MLM) with the Albanian language dataset (alb_dataset) that we have created for this purpose (Kryeziu et al., 2022). We will compare two approaches: training of BERT using the available OSCAR dataset and using our alb_dataset that we have collected. The paper shows some discrepancies during training, especially while evaluating the performance of the model. (shrink)