A thought-provoking webinar on what it means to decolonize anthropology in different contexts around the world. This event brings together Dr. Gretchen Stolte, Dr. Gustavo Lins Ribeiro, and Dr. Carolyn Rouse, contributors of the American Ethnologist special forum, “Decolonizing Anthropology: Global Perspectives”, to explore the past, present, and future of anthropology. This discussion, moderated by Dr. Faye Ginsburg, will touch on everything from the decolonizing politics of citation, public anthropology, the historical relationship between anthropology and colonial power in different parts of the world, and the degree to which anthropology provided and provides the tools to resist.
 
**Panelists**
 
 Dr. Gretchen Stolte, School of Social Sciences, University of Western Australia
 Dr. Gustavo Lins Ribeiro, Departamento de Estudios Culturales, Universidad Autónoma Metropolitana (Mexico), Departamento de Antropologia (emeritus), Universidade de Brasília
 Dr. Carolyn Rouse, Department of Anthropology, Princeton University
 **Moderator**
 
Dr. Faye Ginsburg, Department of Anthropology, New York University

Discussing ‘Decolonizing Anthropology: Global Perspectives'

This webinar brings together scholars involved in the World Anthropologies movement to discuss the current state of this vital project with global scope. Starting with the recognition that the building of anthropological knowledge is mainly constrained both by imperial/colonial structures and the default frame of the nation-state, the session will explore methods of doing anthropology otherwise. As an AAA sponsored event, the session will focus in particular on the role of the US and the AAA. We will reflect on the extent to which USian anthropology positions itself as an (or even sometimes the!) “anthropology without culture”, as well as on how USian institutions and structures produce a USian anthropology. Within the context of efforts from within the AAA and other national or multinational bodies (notably the World Council of Anthropological Associations) and supranational bodies (notably the International Union of Anthropological and Ethnological Sciences), we will discuss the struggle to decenter knowledge production from a place of privilege and domination. We will then consider how the World Anthro movement does or could build bridges with anthropologies that traverse, get around, subvert, or transcend “national” boundaries, and in particular the hegemony of the US, the AAA, and of English, and with movements that seek to queer anthropology, to decolonize it, to crip it, or to burn it down.

World Anthropologies: Building Disciplinary Bridges and Upending Hierarchies of Knowledge

Show Me an Anthropologist and I'll Show You a Librarian

Historical artifacts only have meaning because of the stories we tell through and about them.

What kinds of stories emerge from the artifacts found in the Southeast Chicago Historical Museum? These storylines weave together artifacts, documentary film, and online storytelling. We take objects donated to the museum, explore their meaning for donors, and then interweave these stories with those relating to other Museum objects. Can these extended storylines become launching points for conversations about the nature of work, the value of community, and what it means to be an American? Can they help us reimagine our collective futures?

The Stories We Tell — The Southeast Chicago Archive & Storytelling Project

In this article, we discuss the conditions surrounding the building of historical and literary corpora. We describe the assumptions and method of making the original corpus of the Polish novel (1864-1939). Then we present the research procedure aimed at demonstrating the variability of the emotional value of the concept of ‘the city’ and ‘the country’ in the texts included in our corpus. The proposed method considers the complex socio-political nature of Central and Eastern Europe, especially the fact that there was no unified Polish state during this period. The method can be easily replicated in the studies of the literature of countries with similar specificities.

Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939

Deontic modals are auxiliary verbs which express some kind of necessity, obligation, or moral recommendation. This paper investigates the collocation and distribution within Jane Austen’s six mature novels of the following deontic modals: must, should, ought, and need. We also examine the co-occurrences of these modals with name mentions of the heroines in the six novels, categorizing each occurrence with a category of obligation if applicable. The paper offers a brief explanation of the categories of obligation chosen for this investigation. In order to examine the types of obligations associated with each heroine, we then investigate the distribution of these categories in relation to mentions of each heroine. The patterns observed show a general concurrence with the thematic characterizations of Austen’s heroines which are found in literary analysis.

The Distribution of Deontic Modals in Jane Austen's Mature Novels

Petitions are a rich historical source, yet they have been relatively little used in historical research. In this paper, we aim to analyse Swedish texts from around the 18th century, and petitions in particular, using automatic means of text classification. We also test how text pre-processing and different feature representations affect the result, and we examine feature importance for our main class of interest - petitions. Our experiments show that the statistical algorithms NB, RF, SVM, and kNN are indeed very able to classify different genres of historical text. Further, we find that normalisation has a positive impact on classification, and that content words are particularly informative for the traditional models. A fine-tuned BERT model, fed with normalised data, outperforms all other classification experiments with a macro average F1 score at 98.8. However, using less computationally expensive methods, including feature representation with word2vec, fastText embeddings or even TF-IDF values, with a SVM classifier also show good results for both unnormalised and normalised data. In the feature importance analysis, where we obtain the features most decisive for the classification models, we find highly relevant characteristics of the petitions, namely words expressing signs of someone inferior addressing someone superior.


To the Most Gracious Highness, from Your Humble Servant: Analysing Swedish 18th Century Petitions Using Text Classification

Annotation of unlabeled textual corpora with linguistic metadata is a fundamental technology in many scholarly workflows in the digital humanities (DH). Pretrained natural language processing pipelines offer tokenization, tagging, and dependency parsing of raw text simultaneously using an annotation scheme like Universal Dependencies (UD). However, the accuracy of these UD tools remains unknown for historical texts and current methods lack mechanisms that enable helpful evaluations by domain experts. To address both points for the case of Modern Historical Japanese text, this paper proposes the use of unsupervised domain adaptation methods to develop a domain-adapted language model (LM) that can flag instances of inaccurate UD output from a pretrained LM and the use of these instances to form rules that, when applied, improves pretrained annotation accuracy. To test the efficacy of the proposed approach, the paper evaluates the domain-adapted LM against three baselines that are not adapted to the historical domain. The experiments conducted demonstrate that the domain-adapted LM improves UD annotation in the Modern Historical Japanese domain and that rules produced using this LM are best indicative of characteristics of the domain in terms of out-of-vocabulary rate and candidate normalized form discovery for "difficult" bigram terms.

Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generated corpus with a news corpus, in this case 5,082 articles produced by CBC News between January 23 and May 5, 2020. We find our artificially generated articles exhibits a considerably more negative attitude towards COVID and a significantly lower reliance on geopolitical framing. Our methods and results hold importance for researchers seeking to simulate large scale cultural processes via recent breakthroughs in text generation.

The COVID That Wasn't: Counterfactual Journalism Using GPT

In the news, statements from information sources are often quoted, made by individuals who interact in the news. Detecting those quotes and the gender of their sources is a key task when it comes to media analysis from a gender perspective. It is a challenging task: the structure of the quotes is variable, gender marks are not present in many languages, and quote authors are often omitted due to frequent use of coreferences. This paper proposes a strategy to measure the presence of women and men as information sources in news. We approach the problem of detecting sentences including quotes and the gender of the speaker as a joint task, by means of a supervised multiclass classifier of sentences. We have created the first datasets for Spanish and Basque by manually annotating quotes and the gender of the associated sources in news items. The results obtained show that BERT based approaches are significantly better than bag-of-words based classical ones, achieving accuracies close to 90%. We also analyse a bilingual learning strategy and generating additional training examples synthetically; both provide improvements up to 3.4% and 5.6\%, respectively.

 Measuring Presence of Women and Men as Information Sources in News

Nowadays, the interest in code-mixing has become ubiquitous in Natural Language Processing (NLP); however, not much attention has been given to address this phenomenon for Speech Translation (ST) task. This can be solely attributed to the lack of code-mixed ST task labelled data. Thus, we introduce Prabhupadavani, which is a multilingual code-mixed ST dataset for 25 languages. It is multi-domain, covers ten language families, containing 94 hours of speech by 130+ speakers, manually aligned with corresponding text in the target language. The Prabhupadavani is about Vedic culture and heritage from Indic literature, where code-switching in the case of quotation from literature is important in the context of humanities teaching. To the best of our knowledge, Prabhupadvani is the first multi-lingual code-mixed ST dataset available in the ST literature. This data also can be used for a code-mixed machine translation task. All the dataset can be accessed at this https URL.

Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages

The task of computational textual narrative de- tection focuses on detecting the presence of narrative parts, or the degree of narrativity in texts. In this work, we focus on detecting the local degree of narrativity in texts, using short text passages. We performed a human annota- tion experiment on 325 English texts ranging across 20 genres to capture readers’ perception by means of three cognitive aspects: suspense, curiosity, and surprise. We then employed a linear regression model to predict narrativity scores for 17,372 texts. When comparing our average annotation scores to similar annotation experiments with different cognitive aspects, we found that Pearson’s r ranges from .63 to .75. When looking at the calculated narrative proba- bilities, Pearson’s r is .91. We found that it is possible to use suspense, curiosity and surprise to detect narrativity. However, there are still differences between methods. This does not imply that there are inherently correct methods, but rather suggests that the underlying defini- tion of narrativity is a determining factor for the results of the computational models employed.

Underline digital video library

Discussing ‘Decolonizing Anthropology: Global Perspectives'

World Anthropologies: Building Disciplinary Bridges and Upending Hierarchies of Knowledge

Show Me an Anthropologist and I'll Show You a Librarian

The Stories We Tell — The Southeast Chicago Archive & Storytelling Project

Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939

The Distribution of Deontic Modals in Jane Austen's Mature Novels

To the Most Gracious Highness, from Your Humble Servant: Analysing Swedish 18th Century Petitions Using Text Classification

Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

The COVID That Wasn't: Counterfactual Journalism Using GPT

Measuring Presence of Women and Men as Information Sources in News

Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages

Computational Detection of Narrativity: A Comparison Using Textual Features and Reader Response

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