Endangered languages remain significantly underrepresented in modern Natural Language Processing (NLP) due to the scarcity of foundational computational resources. Although recent advances in multilingual and low-resource NLP have improved language technologies for many underrepresented languages, evaluation commonly emphasizes technical measures such as corpus quality, representation quality, and downstream task performance. While these metrics are essential for assessing computational quality, they provide limited insight into the broader societal impact of NLP resource development, particularly for endangered languages where digital inclusion, cultural preservation, and long-term sustainability are central objectives. In this paper, we propose a conceptual framework for assessing the societal impact of NLP resource development for endangered languages. The framework complements conventional technical evaluation by introducing six dimensions: technical readiness, accessibility, community participation, educational and cultural value, research reusability, and sustainability. We illustrate the application of the framework through the development of computational resources for Bartangi, an endangered Pamiri language, demonstrating how it can be used to identify strengths, limitations, and future priorities beyond technical performance alone. Rather than replacing conventional evaluation, the proposed framework extends it by providing a structured perspective for understanding how foundational NLP resources contribute to responsible AI, digital inclusion, and the long-term computational preservation of endangered languages.

In this paper, we showcase a comprehensive end-to-end pipeline for creating a superior Bartangi language corpus and using it for training word embeddings. The critically low-resource Pamiri lan- guage of Bartangi, which is spoken in Tajikistan, has difficulties such as morphological complexity, orthographic variety, and a lack of data. In order to overcome these obstacles, we gathered a raw corpus of roughly 6,550 phrases, used the Uniparser-Morph-Bartangi morphological analyzer for linguistically accurate lemmatization, and implemented a thorough cleaning procedure to eliminate noise and ensure proper tokenization. The lemmatized corpus that results greatly lowers word spar- sity and raises the standard of linguistic analysis.The processed corpus was then used to train two different Word2Vec models, Skip-gram and CBOW, with a vector size of 100, a context window of 5, and a minimum frequency threshold of 1. The resultant word embeddings were displayed using dimensionality reduction techniques like PCA and t-SNE, and assessed using intrinsic methods like nearest-neighbor similarity tests. Our tests show that even from tiny datasets, meaningful semantic representations can be obtained by combining informed morphological analysis with clean prepro- cessing. One of the earliest computational datasets for Bartangi, this resource serves as a vital basis for upcoming NLP tasks, such as language modeling, semantic analysis, and low-resource machine translation. To promote more research in Pamiri and other under-represented languages, we make the corpus, lemmatizer pipeline, and trained embeddings publicly available.