• Digital Humanities and Cultural Heritage
• 2013-2022close
• Other literature type close
• Publikationer från Umeå universitet close

Collecting and producing mass data has offered an appealing way to condense educational phenomena. However, thus far, little attention has been given to the seemingly insignificant preprinted forms that represent the basis for compiling and aggregating data. Taking inspiration from science and technology studies and the ensuing development of so-called paper technologies, this article highlights the potential of small forms in education that were used to record, evaluate, and aggregate data for educational statistics. By suggesting a multi-level methodological approach that we frame as 3D hermeneutics, we seek to contribute a methodological proposal on how to analyse these materials and showcase what lies beneath – or what comes before – the knowledge produced by educational statistics. These analyses draw on pre-printed forms collected by the Prussian educational administration at the turn of the nineteenth century, and re-trace the contexts they were embedded in, examine their materiality, and reconstruct their usage.

This article explores processes of place-making through the study of the linguistic landscape of a small-size town in Northern Sweden. The analysis of signs is used as a tool for examining the role and visibility of actors in the landscape. For this purpose, we examine who the authors are, what forms of multilingualism can be observed, and who has agency in the place-making of the public space. Our documentation consists of photos and fieldnotes from observations, encounters, and conversations with people during ethnographic fieldwork in 2019. Using a mixed-methods approach, all signs were first analysed quantitatively according to the categories of authors and function. Regression analysis was used to explore correlations between the categories. Secondly, multilingual signs were analysed qualitatively regarding their function and purpose in relation to their contexts. Our results illustrate a city centre with a strong presence of the Swedish language. Multilingual signs target specific groups and are intended for information, advertisement, rules and regulations; moreover, our findings indicate that the opportunities for private actors to influence the linguistic landscape are limited. The form of multilingualism in this context—visible multilingualism present mainly through English—is different from the one we can see in the socio-demographic data.

BackgroundThere is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed.ObjectiveTo understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.MethodsWe conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2–3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs.ResultsTwenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation.ConclusionsMain gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.

Review (English): Johan Samuelsson, Läroverken och progressivismen: Perspektiv på historieundervisningens praktik och policy 1920–1950, Lund: Nordic Academic Press, 2021, 290pp.

The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet changes in the hardware platform and application. This article introduces a Multi-Objective Hardware-Aware Quantization (MOHAQ) method, which considers hardware performance and inference error as objectives for mixed-precision quantization. The proposed method feasibly evaluates candidate solutions in a large search space by relying on two steps. First, post-training quantization is applied for fast solution evaluation (inference-only search). Second, we propose the ”beacon-based search” to retrain selected solutions only and use them as beacons to estimate the effect of retraining on other solutions. We use speech recognition models on TIMIT dataset. Experimental evaluations show that Simple Recurrent Unit (SRU)-based models can be compressed up to 8x by post-training quantization without any significant error increase. On SiLago, we found solutions that achieve 97% and 86% of the maximum possible speedup and energy saving, with a minor increase in error on an SRU-based model. On Bitfusion, the beacon-based search reduced the error gain of the inference-only search on SRU-based models and Light Gated Recurrent Unit (LiGRU)-based model by up to 4.9 and 3.9 percentage points, respectively.

The mining industry is an important part of the society’s green transition by providing the metals and minerals necessary for an increased electrification. One downside of the mining industry is that the mining operation typically contaminates the land to some degree. Excavation-and-refill is a traditional remediation approach for soil contaminations, a method that often could be very expensive and cause a large amount of greenhouse gas emissions. This study aims to compare different excavation strategies to find out how they affect the sustainability dimensions and the climate. This is done by using two measuring tools: SAMLA for contaminated sites and Carbon footprint of remediations and other groundworks. The results show that the largest greenhouse gas emissions occur when transporting the contaminated soils to the designated landfill. The different excavation approaches have a positive impact primarily on the ecological and social sustainability dimensions, including reducing the risks for the environment and people’s health. To make excavation-and-refill remediations more sustainable the main focus should be towards the largest contributor of greenhouse gas emissions, together with developing the knowledge regarding the climate impact of remediation methods and to improve the knowledge of alternative, more sustainable, remediation methods. Implementation of environmental regulations focusing on remediations could also be an important factor in the transition towards more sustainable remediation methods.