Annotation Practices Research Articles

Events in context—The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

Offensive language detection in low resource languages: A use case of Persian language.

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Different types of abusive content such as offensive language, hate speech, aggression, etc. have become prevalent in social media and many efforts have been dedicated to automatically detect this phenomenon in different resource-rich languages such as English. This is mainly due to the comparative lack of annotated data related to offensive language in low-resource languages, especially the ones spoken in Asian countries. To reduce the vulnerability among social media users from these regions, it is crucial to address the problem of offensive language in such low-resource languages. Hence, we present a new corpus of Persian offensive language consisting of 6,000 out of 520,000 randomly sampled micro-blog posts from X (Twitter) to deal with offensive language detection in Persian as a low-resource language in this area. We introduce a method for creating the corpus and annotating it according to the annotation practices of recent efforts for some benchmark datasets in other languages which results in categorizing offensive language and the target of offense as well. We perform extensive experiments with three classifiers in different levels of annotation with a number of classical Machine Learning (ML), Deep learning (DL), and transformer-based neural networks including monolingual and multilingual pre-trained language models. Furthermore, we propose an ensemble model integrating the aforementioned models to boost the performance of our offensive language detection task. Initial results on single models indicate that SVM trained on character or word n-grams are the best performing models accompanying monolingual transformer-based pre-trained language model ParsBERT in identifying offensive vs non-offensive content, targeted vs untargeted offense, and offensive towards individual or group. In addition, the stacking ensemble model outperforms the single models by a substantial margin, obtaining 5% respective macro F1-score improvement for three levels of annotation.

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data

Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names to over 90% of unique assemblies in the PDB and provided persistent identifiers for each assembly. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of macromolecular complexes. Furthermore, the data standardisation improves the PDB’s FAIR attributes, fostering more effective basic and translational research and scientific education.

Annotation uncertainty in the context of grammatical change

Abstract This paper elaborates on the notion of uncertainty in the context of annotation in large text corpora, specifically focusing on (but not limited to) historical languages. Such uncertainty might be due to inherent properties of the language, for example, linguistic ambiguity and overlapping categories of linguistic description, but could also be caused by a lack of annotation expertise. By examining annotation uncertainty in more detail, we identify the sources, deepen our understanding of the nature and different types of uncertainty encountered in daily annotation practice, and discuss practical implications of our theoretical findings. This paper can be seen as an attempt to reconcile the perspectives of the main scientific disciplines involved in corpus projects, linguistics and computer science, to develop a unified view and to highlight the potential synergies between these disciplines.

A Functional Classification of Text Annotations for Engineering Design

Describing and supplementing geometric shapes (parts) and layouts (assemblies) with relevant information is key for successful product design communication. 3D annotation tools are widely available in commercial systems, but they are generally used in the same manner as 2D annotations in traditional engineering drawings. The gap between technology and practices is particularly evident in plain text annotations. In this paper, we introduce a functional classification of text annotations to provide an information framework for shifting traditional annotation practices towards the Model-Based Definition (MBD) paradigm. In our view, the current classification of dimensions, tolerances, symbols, notes, and text does not stress the inherent properties of two broader categories: symbols and text. Symbol-based annotations use a symbolic language (mostly standardized) such as Geometric Dimensioning and Tolerancing (GD&T) to provide precise information about the implications of geometric imperfections in manufacturing, whereas notes and text are based on non-standardized and unstructured plain text, and can be used to convey design information. We advocate that text annotations can be characterized in four different functional types (objectives, requirements, rationale, and intent), which should be classified as such when annotations are added to a model. The identification and definition of a formalized structure and syntax can enable the management of the annotations as separate entities, thus leveraging their individual features, or as a group to gain a global and collective view of the design problem. The proposed classification was tested with a group of users in a redesign task that involved a series of geometric changes to an annotated assembly model.

MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation.

We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images, requiring more than 1.34 years of collective annotator effort. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training. We evaluate our models in the 2020 Robust Vision Challenge (RVC) as an extreme generalization experiment. MSeg training sets include only three of the seven datasets in the RVC; more importantly, the evaluation taxonomy of RVC is different and more detailed. Surprisingly, our model shows competitive performance and ranks second. To evaluate how close we are to the grand aim of robust, efficient, and complete scene understanding, we go beyond semantic segmentation by training instance segmentation and panoptic segmentation models using our dataset. Moreover, we also evaluate various engineering design decisions and metrics, including resolution and computational efficiency. Although our models are far from this grand aim, our comprehensive evaluation is crucial for progress. We share all the models and code with the community.

Feature: Teaching Reading as Raciolinguistic Justice: (Re)Centering Reading Strategies for Antiracist Reading

Antiracist education practices have gained increasing attention. Oftentimes, however, descriptions of this work fail to explicate the role of reading skills in students’ critical engagement with diverse texts. I explore the potential of metacognitive reading strategy instruction as a form of foundational literacy skills development for engaging in antiracist reading. Drawing from my experiences as a female of color and a coordinator and instructor of integrated reading and writing, I expand upon the concept of raciolinguistically just reading instruction, describing how students can document their application of multiple foundational reading strategies through the meta-strategy of annotation and other metacognitive practices. In particular, I focus on how students’ annotations can reflect their work in making text-based connections. Such annotation practice enacts a culturally sustaining pedagogy that amplifies student voices and their role as knowledge producers. I conclude by considering the larger role of decentering the instructor to foster students’ antiracist reading.

O-125 Application of artificial intelligence using big data to devise and train a machine learning model on over 63,000 human embryos to automate time-lapse embryo annotation

Abstract Study question Can a machine learning (ML) model, developed using modern neural network architecture produce comparable annotation data; utilisable for algorithmic outcome prediction, to manual time-lapse annotations? Summary answer The model automatically annotated unseen embryos with comparable results to manual methods, generating morphokinetic data to enable comparably predictive outputs from an embryo selection algorithm. What is known already The application of artificial intelligence across healthcare industries, including fertility, is increasing. Several ML models are available that seek to generate or analyse embryo images and morphokinetic data, and to determine embryo viability potential. Along with photographic images, the use of time-lapse in IVF laboratories has amassed numeric data, resulting predominantly from annotated manual assessment of images over time. Embryo annotation practice is variable in quality, can be subjective and is time-consuming; commonly taking several minutes per embryo. The development of rapid, accurate automatic annotation would represent a significant time-saving as well as an increase in reproducibility and accuracy. Study design, size, duration Multicentre quality assured annotation data from 63,383 time-lapse monitored embryos (EmbryoScope®), comprising over 400 million individual images, were used to train a ML model to automatically generate morphokinetic annotations. Data was derived from 8 UK clinics within a cohesive group between 2012-2021. Accuracy was assessed using 900 unseen embryos (with live birth outcome) by comparing the output of an established in-house, prospectively validated embryo selection model when the input was either ML-automated, or manual annotations. Participants/materials, setting, methods Multi-focal plane images were processed on the Azure cloud (Microsoft) and resampled to 300x300 pixels. A Laplacian-based focal stacking algorithm merged frames into a single image. The model consisted of an EfficientNetB4 Convolutional Neural Network classifier to extract features and classify the stage of embryo images. A Temporal Convolutional Network interpreted a time-series of image features; producing annotations from pronuclear fading through to blastocyst. Soft localisation loss function used QA data to integrate annotation subjectivities. Main results and the role of chance The ML model rapidly and automatically generated annotations. Efficacy and comparability of the ML model to automate reliable, utilisable annotations was demonstrated by comparison with manual annotation data and the ML model’s ability to auto-generate annotations which could be used to predict live birth by providing annotation data to an established, validated in house embryo selection model. Live birth-predictive capability was measured, and benchmarked against manual annotation, using the area under the receiver operating characteristic curve (AUC). When tested on time-lapse images, collected from pronuclear fading to full blastulation, representing 900 previously unseen, transferred blastocysts where live birth outcomes were blinded, the in-house developed auto-annotation ML model resulted in an AUC of 0.686 compared with 0.661 for manual annotations, for live birth prediction. Auto annotation using the developed model took only milliseconds to complete per embryo. The developed auto-annotation model, built and tested on large data, is considered suitable for productionisation with the aim of being validated and integrated into an application to support IVF laboratory practice. Limitations, reasons for caution Whilst this model was trained to recognise key morphokinetic events, there are other morphokinetic variables that may be useful in the prediction of live birth and further improve embryo selection, or deselection, ability. Akin to manual interpretation, some embryos may fail to be annotated or need second opinion. Wider implications of the findings There is increasing evidence supporting the application of ML to utilise big data from time-lapse imaging and fertility care generally. Whilst promising benefits to IVF clinics and patients, responsible use of data is required alongside large high-quality datasets, and rigorous validation, to ensure safe and robust applications. Trial registration number N/A

An Example of Ottoman Scholars’ Knowledge of Tajwīd: Content Analysis of Kitābü Maslaki’l-Kurrā’ fī sharhi abyāti Ibni’l-Cezerī fī ‘ilmi’l-kırā’a

The Prophet interpreted the verses verbally and conveyed it to his Companions as he heard it. In the early periods, the issues that came under the scrutiny of the science of tajwīd existed in a compelling way with the Arabic language, tafsīr and the science of the different readings of the Qur’ān studies. We do not have any data on the use of tajwīd rules in poetry or any other literary text among Arabs before the Qur'ān. Pronouncing a text in the middle in Arabic within the framework of some reading rules is a matter that occurred with the revelation of the Qur'ān. With the beginning of the compilation of sciences, the science of tajwīd emerged as a separate science from the fifth century AH, and works on this science began to be copyrighted. In this respect, the ulamā, who produced detached works on the science of tajwīd, emerged in the central cities of Islamic lands, especially in the knowledge of Andalusia. The existence of these works did not cause any weakening in the practice of direct recitation of the Qur'ān from a competent teacher. Although competent works have been written since the early periods, the theorists of the science of tajwīd have always emphasized the importance and necessity of obtaining this knowledge from a knowledgeable teacher in the books they wrote. Ibn al-Jazarī, who collected all the information written before him on the science of Qiraat and tajwīd, and produced works that are easy to follow and apply in the education and training of Qur'ān readings and recitation. He wrote some of them in verse to facilitate memorization. It should be said that there was a great breakthrough in the Islamic world of the Ottoman period, as well as in all other Islamic sciences, by establishing centers in the science of tajwīd and appointing competent teachers from all over the world to head these educational institutions. In this article, the commentary made by a tenth/sixteenth Ottoman scholar who introduced himself as the Imam of the Mosque of Cadīd, to the work of Ibn al-Jazarī called Mukaddima, which was used as a bedside book in the teaching of tajwîd for centuries in the Ottoman period, examined. The aim of the research is to present a serious example that the practice of commentary and annotation, which was established as a method of acquiring and transferring knowledge in the Ottoman Empire, is not just a repetition of old sciences, as it is thought, but also the building blocks of a new revival and even construction process by revealing extensive scientific knowledge. In this way, it has been tried to reveal that the commentary, which is examined by looking at the vast tradition of commentary on the Mukaddima in the Ottoman Empire, is one of the complementary elements of a tradition. It is also aimed to present an important example of the influence of Ibn al-Jazarī's students and the effects of the works he wrote on Ottoman science and especially in the circles of qiraat and tajwīd since the day he was allocated a dār al-kurrā in Bursa. In the research, a method based on going to the source, finding the place of the issue in other sciences mentioned in the main source, and reconsidering and criticizing with new findings when necessary was used.

Tyto: A Python Tool Enabling Better Annotation Practices for Synthetic Biology Data-Sharing.

As synthetic biology becomes increasingly automated and data-driven, tools that help researchers implement FAIR (findable-accessible-interoperable-reusable) data management practices are needed. Crucially, in order to support machine processing and reusability of data, it is important that data artifacts are appropriately annotated with metadata drawn from controlled vocabularies. Unfortunately, adopting standardized annotation practices is difficult for many research groups to adopt, given the set of specialized database science skills usually required to interface with ontologies. In response to this need, Take Your Terms from Ontologies (Tyto) is a lightweight Python tool that supports the use of controlled vocabularies in everyday scripting practice. While Tyto has been developed for synthetic biology applications, its utility may extend to users working in other areas of bioinformatics research as well. Tyto is available as a Python package distribution or available as source at https://github.com/SynBioDex/tyto.

Self-admitted technical debt practices: a comparison between industry and open-source

Self-admitted technical debt (SATD) consists of annotations, left by developers as comments in the source code or elsewhere, as a reminder about pieces of software manifesting technical debt (TD), i.e., “not being ready yet”. While previous studies have investigated SATD management and its relationship with software quality, there is little understanding of the extent and circumstances to which developers admit TD. This paper reports the results of a study in which we asked developers from industry and open-source about their practices in annotating source code and other artifacts for self-admitting TD. The study consists of two phases. First, we conducted 10 interviews to gather a first understanding of the phenomenon and to prepare a survey questionnaire. Then, we surveyed 52 industrial developers as well as 49 contributors to open-source projects. Results of the study show how the TD annotation practices, as well as the typical content of SATD comments, are very similar between open-source and industry. At the same time, our results highlight how, while open-source code is spread of comments admitting the need for improvements, SATD in industry may be dictated by organizational guidelines but, at the same time, implicitly discouraged by the fear of admitting responsibilities. Results also highlight the need for tools helping developers to achieve a better TD awareness.

Imagining Marshall McLuhan as a digital reader: an experiment in applied Joyce

ABSTRACT Was Marshall McLuhan's greatest work not his published writing but his reading? This article examines McLuhan's annotation practices in his copies of James Joyce's Ulysses and A Portrait of the Artist as a Young Man, and asks what kinds of evidence a prolific annotator like McLuhan might have left if he had done his reading digitally. The first half profiles McLuhan's annotation practices, illustrated by examples from books from McLuhan's personal library. The second half describes an informal experiment, in which the author tests McLuhan's annotation techniques against the capabilities of digital book platforms and formats, specifically PDF (using the Apple Preview app) and EPUB (using the Apple Books app). Overall, the article works through two questions that are relevant to book history, marginalia studies, and digital archiving. How will we understand the evidence left behind by readers when that evidence is in born-digital forms, as archived digital files? And what can we learn about digital reading and annotation from someone like McLuhan, who did those things exceptionally well in another medium? The conclusion reflects on prospects for the study of born-digital marginalia, and on the role of encyclopedism in understanding both McLuhan's and Joyce's media theory.

Visual exploration of historical maps

AbstractHistorical maps tell rich stories, and they are also valuable data objects. They vary greatly in size, type, and context, as well as the kinds and density of information they contain. Historical maps are indeed objects that invite close reading, interpretation, and debate. Whereas a variety of environments exist for the annotation, manipulation, and representation of digital maps, or map-derived data, workflows in the spatial digital humanities can be complex and those environments are not often well integrated. In this article, we describe a prototype named ‘MapFolder’ for studying maps, its algorithms for calculating the areal distortion, its visual design for communicating that distortion, along with a scholarly workflow. We blend annotation practices common in the spatial humanities with the workflows of georeferencing in order to be able to visualize how historic cartographic documents compare with the geospatial representations we are familiar with today. The case studies we use to demonstrate ‘MapFolder’ are maps of the medieval period, a body of maps that are less often studied algorithmically and that are usually avoided in typical workflows of georeferencing. MapFolder is by no means a prototype designed to work exclusively with medieval maps, but since maps of this period are only partially geographic in their design, they offer a particularly fruitful opportunity to rethink the algorithmic manipulation of historical depictions of the world. Working with this complex data from the humanities allows us, as well, to propose the use of visualization for critical, comparative spatial analysis in pre-modern studies and beyond.

Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.

The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, more precisely, detect small intestinal abnormalities like bleeding. In particular, we explore a convolutional neural network (CNN)-based deep learning framework to identify bleeding and non-bleeding CE images, where a pre-trained AlexNet neural network is used to train a transfer learning CNN that carries out the identification. Moreover, bleeding zones in a bleeding-identified image are also delineated using deep learning-based semantic segmentation that leverages a SegNet deep neural network. To evaluate the performance of the proposed framework, we carry out experiments on two publicly available clinical datasets and achieve a 98.49% and 88.39% F1 score, respectively, on the capsule endoscopy.org and KID datasets. For bleeding zone identification, 94.42% global accuracy and 90.69% weighted intersection over union (IoU) are achieved. Finally, our performance results are compared to other recently developed state-of-the-art methods, and consistent performance advances are demonstrated in terms of performance measures for bleeding image and bleeding zone detection. Relative to the present and established practice of manual inspection and annotation of CE images by a physician, our framework enables considerable annotation time and human labor savings in bleeding detection in CE images, while providing the additional benefits of bleeding zone delineation and increased detection accuracy. Moreover, the overall cost of CE enabled by our framework will also be much lower due to the reduction of manual labor, which can make CE affordable for a larger population.

Study of Healthcare Annotation Systems

The annotation practice is an almost daily activity used by healthcare professionals (PHC) to analyze patients' records, collaborate, share knowledge, and communicate. These annotations are generated within a healthcare cycle. Similarly, this cycle represents the life cycle of annotations in the patient record. The exponential increase in the number of medical annotation systems made the choice of a system by a PHC difficult, in a well-defined context (biology, radiology) and according to his/her needs to the functionalities offered by these tools. Therefore, the authors propose two taxonomies to distinguish annotation tools developed by industry and academia over the last two decades. The first classification provides an external vision based on five generic criteria. The second classification is an internal vision that gives us an idea about the functionalities offered by these systems. Finally, these unified and integrated classifications criteria are used to organize and observe the limitation of 50 medical annotation tool systems.

Scolding the child who threw the scissors: Shaping discourse expectations by restricting referents

ABSTRACT Coherence relations are often assumed to hold between clauses, but restrictive relative clauses (RCs) are usually not granted discourse segment status because they are syntactically and conceptually integrated in their matrix clauses. This paper investigates whether coherence relations can be inferred between restrictive RCs and their matrix clauses. Three experiments provide converging evidence that restrictive RCs can indeed play a role at the discourse level and should not categorically be excluded from receiving discourse segment status in discourse annotation practices. At the same time, the studies provide new insights into implicit causality verb biases, specifically about next-mention biases in concessive coherence relations, and expectations about discourse structure, upcoming referents, and upcoming coherence relations.

An audit of radiographers\u2019 practice of left-right image annotation in film-screen radiography and after installation of computed radiography in a tertiary hospital in Africa

BackgroundErrors in radiographic image annotation by radiographers could potentially lead to misdiagnoses by radiologists and wrong side surgery by surgeons. Such medical negligence has dire medico-legal consequences. It was hypothesized that newer technology of computed radiography (CR) and direct digital radiography (DDR) image annotation would potentially lead to a change in practice with subsequent reduction in annotation errors. Following installation of computed radiography, a modality with electronic, post-processing image annotation, the hypothesis was investigated in our study centre.ResultsA total of 72,602 and 126,482 images were documented for film-screen radiography (FSR) and computed radiography (CR), respectively in the department. From these, a sample size of 9452 made up of 4726 each for FSR and CR was drawn. Anatomical side marker errors were common in every anatomy imaged, with more errors seen in FSR (4.6%) than CR (0.6%). Collectively, an error rate of 3.0% was observed. Errors noticed were as a result of marker burnout due to over-exposure as well as marker cone off due to tight beam collimation.ConclusionError rates were considerably reduced following a change from film-screen radiography (FSR) to computed radiography (CR) at the study centre. This change was, however, influenced more by a team of quality control radiographers stationed at CR workstation than by actual practice in x-ray imaging suite. Presence of anthropomorphic phantom in the teaching laboratories in the universities for demonstrations will significantly inculcate the skill needed to completely eliminate anatomical side marker (ASM) error in practice.

Assessing inter-annotator agreement from collaborative annotation campaign in marine bioacoustics

It is currently widely recognized that automated methods are crucial to help processing long-term recordings of marine bioacoustics. To evaluate the efficiency of such methods, it is essential to develop large-scale annotated datasets. However, besides being laborious and resource intensive, recent studies have suggested that such a task could also be highly subjective with the generation of annotator specific errors.In this work, we investigate the question of inter-annotator agreement from a multi-annotator annotation campaign performed on a marine bioacoustics dataset. After providing quantitative evidence of inter-annotator variability, we investigate potential sources on both the user annotation practice and the annotation data and task to better understand why and how such variability occurs. Our study reveals that the acoustic event type, the Signal-to-Noise Ratio of the acoustic event and the annotator profile are three examples of critical factors impacting the annotation results of a multi-annotator campaign.

When Are Search Completion Suggestions Problematic?

Problematic web search query completion suggestions-perceived as biased, offensive, or in some other way harmful-can reinforce existing stereotypes and misbeliefs, and even nudge users towards undesirable patterns of behavior. Locating such suggestions is difficult, not only due to the long-tailed nature of web search, but also due to differences in how people assess potential harms. Grounding our study in web search query logs, we explore when system-provided suggestions might be perceived as problematic through a series of crowd-experiments where we systematically manipulate: the search query fragments provided by users, possible user search intents, and the list of query completion suggestions. To examine why query suggestions might be perceived as problematic, we contrast them to an inventory of known types of problematic suggestions. We report our observations around differences in the prevalence of a) suggestions that are problematic on their own versus b) suggestions that are problematic for the query fragment provided by a user, for both common informational needs and in the presence of web search voids-topics searched by few to no users. Our experiments surface a rich array of scenarios where suggestions are considered problematic, including due to the context in which they were surfaced. Compounded by the elusive nature of many such scenarios, the prevalence of suggestions perceived as problematic only for certain user inputs, raises concerns about blind spots due to data annotation practices that may lead to some types of problematic suggestions being overlooked.

Voetnootpraktijken

Abstract On footnotes Footnotes are a crucial part of the historian’s craft. Yet, they are often construed as no more than tools, used by historians, and scientists, to refer to sources and relevant literature. By looking at several student handbooks on history writing and recent studies on annotation practices, we argue in this discussion article that footnotes are more than simple references to other people’s work and that historians would do well to reflect on this. Footnotes can help historians construct historiographical and scientific discussions, and are therefore inextricably linked to politics of representation, inclusion and exclusion. Footnotes, we argue, fulfill a social and political function as knowledge brokers. With the rise of citation indexes problems of self-citation and gender misattribution have recently received more attention. Research into the uses and misuses of footnotes can shine a light on citation practices that reflect inequalities within academia. Therefore, it is worth considering more closely what happens in the literal margins of history writing.