Search

This article is about current policy in the coordination of opportunities for adult migrants in England to learn English. People who move to a different country experience a need to learn the dominant language of their new environment, to support their settlement. A willingness to learn the language is a marker of social inclusion from a political perspective too: an insistence that migrants have an obligation to learn and use the language is a recurrent trope in political and media discourse. In the UK, language education for adult migrants focuses on the area of education known as ESOL, English for Speakers of Other Languages. Beyond the rhetoric, policy support for migrants' learning of English across the UK is inconsistent: there is neither a UK-wide nor an England-specific strategy in policy to support access to ESOL. Where policy exists, it is formed at a local level in the absence of national direction. The aim of this paper is to consider how an important area of adult education appears to have little presence in national policy, and what the implications of this are, for practice. To achieve this, we first follow the trajectory of ESOL policy in England, considering why--despite attempts to address its coordination--there remains a lacuna. Second, we ask what the implications are of this policy gap for ESOL coordination in practice. Analysis of current policy and of interviews with key ESOL stakeholders suggests an enduring condition of fragmentation and lack of coordination to the detriment of students.

As society embraces technology to support collaboration anywhere and at anytime, there is a growing opportunity for artificial agents to support such collaboration. However, little seems known about how such agents impact the behavioural performance of human teams. To answer this, we devised a Wizard of Oz study where teams of 3 participants located and corralled targets into a containment area in a virtual desert environment. The Wizard played the role of an artificial intelligent operator who had a map view showing the location of participants, targets and the containment area, and could verbally communicate this information. The Wizard operated under two conditions: they could solely use the map view to decide what responses to utter (non-responsive interaction) or could also listen to participant queries (responsive interaction). The results revealed that participant performance was unaffected by responsive interaction condition, despite having a significantly more favourable perception of a responsive agent.

In many team-based activities, members search for information to gain situational awareness and thereby structure their own behavior. The extent to which members are coupled and in control of their surrounding environment can be accessed via the fluctuations of their searching behaviors. To facilitate prospective control, assistive technologies such as a head-up display (HUD) can alleviate the demands of search and facilitate team performance. This study investigated how three-person teams divided their labor and structured their search behavior when playing a multiplayer search-and-retrieval task where first-person visibility and access to a HUD were manipulated. Results showed that access to task relevant information facilitated performance and division of labor, and increased prospective control of searching behaviors, as indexed by detrended fluctuation analysis (DFA). Over multiple sessions, teams learned to use the HUD to structure their behavior to achieve the task goal. Results indicate the potential in using DFA for monitoring prospective control in team contexts.

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.

This paper investigates a monolingual approach to the teaching of linguistic minority pupils in an English primary school at Key Stage Two (7-11 years old). The work is based on a longitudinal case study of one Russian-speaking migrant pupil and her schooled experience. The analysis and discussion explicate the prohibition of the first or home language (Russian) in the school, and reveal how denying a seven-year-old migrant child permission to use her L1 is detrimental to her learning experience and her well-being. The focal data derive from participant-observation fieldnotes, visual artefacts and interviews with the child, her mother, and a class teacher over a 7-month period. Through an analysis of the participants' stancetaking, we show how the pupil's voice is inaudible in her struggle against a monolingual attitude towards her bilingualism and multicompetence. Our contribution therefore builds on work in critical migrant language education, to identify the importance of enabling the presence of the L1 in learning for migrant pupils.

This article is about navigating asylum, employment and language policy in a new country as an asylum seeker. Through the story of one individual, we show that profound inequalities are exacerbated when forced migrants are limited in their choice of language they might study or use. The individual is Tailor F, an Iraqi man seeking asylum, and the country is Finland, officially bilingual, with a majority language (Finnish) and a minority language (Swedish). Finland's official bilingualism does not extend evenly to language education provided for asylum seekers, who are taught Finnish regardless of the region where they are placed. Upon arrival, Tailor F was housed in a reception centre for asylum seekers located in a Swedish-dominant rural area of the country. Through our linguistic ethnography we examine how he navigates multilingually in his early settlement, his current work and his online life. We relate his story to explicit and implicit official bilingualism in Finland and discuss his lived experiences in relation to the contexts of asylum policy and employment. Tailor F's story shows how, through his practices, he has contested implicit language policy for asylum seekers in order to gain membership of the local Swedish-dominant community, achieve a sense of belonging, and potentially realise his aspirations for the future.

ABSTRACT According to the dogmatism puzzle, for any S and any p, if S knows that p, then she is entitled to be dogmatic about p, and so disregard any evidence against p, for she knows that (or is in a position to know that) that evidence is misleading. But this seems clearly problematically dogmatic. The standard solution to the dogmatism puzzle involves appealing to the view that acquiring new evidence (even misleading evidence) can undermine one's knowledge that p. That is why one cannot rightly disregard any future evidence against p. This solution to the dogmatism puzzle has come to be called “the defeat solution.” Maria Lasonen‐Aarnio has recently argued, however, that the defeat solution leaves unsolved a partial defeat version of the dogmatism puzzle, where some subject acquires weak misleading evidence against p, but, since it is weak, it does not rob her of knowledge that p. Lasonen‐Aarnio argues that solving this partial defeat version of the dogmatism puzzle requires those who endorse the defeasibility of knowledge to either go dogmatist or reject an extremely plausible principle that she calls “Entitlement” (roughly, for any S and any e, if S knows that evidence e is misleading, then S can rightly disregard e). In this paper, however, I argue that defeasibilists face no such challenge from any version of the dogmatism puzzle, since the dogmatism puzzle, in both its original and partial defeat form, rests on an assumption that we have very good reason to think is mistaken. Specifically, the assumption that, for any S and any p, if S knows that p, then S knows (or is in a position to know) that any evidence against p is misleading. I further argue that rejecting this assumption also yields a neat solution to the dogmatism puzzle involving intention originally proposed by Saul Kripke and recently adapted by R.E. Fraser. [ABSTRACT FROM AUTHOR]

Background: Peripheral vascular interventions (PVIs) performed under procedural sedation and analgesia (PSA) can be associated with anxiety and poor compliance with patient instructions during surgery. Mind-body interventions (MBIs) such as meditation have demonstrated the potential to decrease perioperative anxiety, though this area is understudied, and no tailored interventions have been developed for the vascular surgical patient population. Objectives: We aimed to design a perioperative MBI that specifically targeted vascular surgical patients undergoing PVIs under PSA. We sought to perform this in a scientifically rigorous, multi-disciplinary collaborative manner. Methods: Following the Obesity-Related Behavioral Intervention Trials (ORBIT) model, we designed (Phase 1a) and then refined (Phase 1b) a MBI for patients undergoing PVIs under PSA to decrease perioperative anxiety and sedation and facilitate patient intraoperative compliance. Phase 1a involved a literature review, informal information gathering and synthesis, and drafting a preliminary protocol for a perioperative MBI. Phase 1b involved assembling a multi-disciplinary expert panel of perioperative and mind-body clinicians and researchers to improve the MBI using an iterative, modified Delphi approach. Results: The modified Delphi process was completed, and a consensus was reached after three iterations. The resulting MBI consisted of two seven-minute preoperative guided meditations on the day of surgery, including diaphragmatic breathing, body scans, and guided imagery emphasizing awareness of the ipsilateral leg where the vascular surgery was performed. A document delineating the integration of the MBI into the operating room workflow was produced, including details regarding the intervention's timing, duration, and modality. Conclusion: Using a multi-specialty expert panel, we designed a novel MBI in the form of a guided meditation with elements of mindfulness and guided imagery to decrease anxiety and increase intraoperative compliance for patients undergoing PVIs under PSA. A prospective pilot study is being planned to test the program's feasibility. [ABSTRACT FROM AUTHOR]

In this article the author analyses the communicative demands placed on migrants navigating immigration law in a fast-moving policy environment and implications for adult migrant language education. Data are from an ethnographic study of a lawyer, Lucy, and her clients at a legal advice service in Leeds, England, and include interviews and recordings of lawyer-client interactions. The analytical focus is on Lucy's stance (Jaffe, 2009b), on how she presents herself as an ally of her multilingual clients, and on the stance-marking strategies she and her clients use as they strive to make meaning. The study took place in 2016, a time of volatility for the policies that impinge on immigration law and on legal interaction for migrants: the upsurge of right-wing populist movements in Europe, erratic positions on migration in the United States, and the referendum that decided the United Kingdom would leave the European Union. The author maintains that the link is rarely drawn between interaction in legal and other institutional settings and the content of language classes designed to aid adult migrant settlement, and argues for an approach to adult migrant language education that critically addresses this point.

This paper focuses on an innovative transdisciplinary educational arts-based learning project, LangScape Curators, which links to and leads from research conducted for the AHRC-funded "Translation and Translanguaging" project. Here, we describe how we work collaboratively with creative practitioners to use a variety of creative arts methods with young people to explore the linguistic landscapes of Leeds. We propose a theoretical framework for collaborative research activity of this nature, and we use one of the creative arts activities--collage--to exemplify visual understandings of how communicative repertoires and linguistic landscapes are explored through co-produced pedagogical workshops. The programme and its associated research make an original contribution to linguistic landscape-based collaborative ethnographic research. We conclude by setting out directions for the future of these activities and their application in applied linguistics research and practice.

Classification of high-latitude landscapes into their appropriate biomes is important for many climate and global change-related issues. Unfortunately, large-scale, high-spatial-resolution observations of plant assemblages associated with these regions are generally unavailable, so accurate modeling of plant assemblages and biome boundaries is often needed. We built different discriminant analysis models and used them to “convert” various combinations of spatial climatic data (surface temperature and precipitation) and spatial environmental data (topography, soil, permafrost) into a biome-level map of Alaska. Five biomes (alpine tundra and ice fields, Arctic tundra, shrublands, boreal forest, and coastal rainforest) and one biome transition zone are modeled. Mean annual values of climatic variables were less useful than their annual extrema in this context. A quadratic discriminant analysis, combined with climate, topography, permafrost, and soil information, produced the most accurate Alaskan biome classification (skill = 74% when compared to independent data). The multivariate alteration detection transformation was used to identify Climatic Transition Zones (CTZs) with large interannual variability, and hence, less climatic consistency than other parts of Alaska. Biome classification was the least accurate in the CTZs, leading to the conclusion that large interannual climatic variability does not favor a unique biome. We interpret the CTZs as “transition biome areas” or ecotones between the five “core biomes” cited above. Both disturbance events (e.g., fires and subsequent plant succession sequences) and the partial intersection of the environmental variables used to characterize Alaskan biomes further complicate biome classification. Alaskan results obtained from the data-driven quadratic discriminant model compare favorably (based on Kappa statistics) with those produced by an equilibrium-based biome model for regions of Canada ecologically similar to the biomes we studied in Alaska. Climatic statistics are provided for each biome studied.

Maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada, produced by Oregon State University's Spatial Climate Analysis Service (SCAS) and the Alaska Geospatial Data Clearinghouse (AGDC), were analyzed. Because both sets of maps are generally available and in use by the community, there is a need to document differences between the processes and input data sets used by the two groups to produce their respective set of maps and to identify similarities and differences between the two sets of maps and possible reasons for the differences. These differences do not affect the observed large-scale patterns of seasonal and annual variability. Alaska is divided into interior and coastal zones, with consistent but different variability, separated by a transition region. The transition region has high interannual variability but low long-term mean variability. Both data sets support the four major ecosystems and ecosystem transition zone identified in our earlier work. Differences between the two sets of maps do occur, however, on the regional scale; they reflect differences in physiographic domains and in the treatment of these domains by the two groups (AGDC, SCAS). These differences also provide guidance for an improved observational network for Alaska. On the basis of validation with independent in situ data, we conclude that the data set produced by SCAS provides the best spatial coverage of Alaskan long-term mean monthly surface temperature and precipitation currently available.

Accurate prediction of available water supply from snowmelt is needed if the myriad of human, environmental, agricultural, and industrial demands for water are to be satisfied, especially given legislatively imposed conditions on its allocation. Robust retrievals of hydrologic basin model variables (e.g., insolation or areal extent of snow cover) provide several advantages over the current operational use of either point measurements or parameterizations to help to meet this requirement. Insolation can be provided at hourly time scales (or better if needed during rapid melt events associated with flooding) and at 1-km spatial resolution. These satellite-based retrievals incorporate the effects of highly variable (both in space and time) and unpredictable cloud cover on estimates of insolation. The insolation estimates are further adjusted for the effects of basin topography using a high-resolution digital elevation model prior to model input. Simulations of two Sierra Nevada rivers in the snowmelt seasons of 1998 and 1999 indicate that even the simplest improvements in modeled insolation can improve snowmelt simulations, with 10%-20% reductions in root-mean-square errors. Direct retrieval of the areal extent of snow cover may mitigate the need to rely entirely on internal calculations of this variable, a reliance that can yield large errors that are difficult to correct until long after the season is complete and that often leads to persistent underestimates or overestimates of the volumes of the water to operational reservoirs. Agencies responsible for accurately predicting available water resources from the melt of snowpack [e.g., both federal (the National Weather Service River Forecast Centers) and state (the California Department of Water Resources)] can benefit by incorporating concepts developed herein into their operational forecasting procedures.

The transport of fine-grained Asian dust from its source (e.g., the Gobi Desert, Mongolia) to North America is a common springtime phenomenon. Because of its chemical composition (silicon, iron, aluminum, and calcium) and its particle size distribution (mean aerodynamic diameter 2-4 mum), Asian dust produces a negative signal in the split-window T-4 - T-5 algorithm, as does airborne volcanic ash. The split-window algorithm is commonly used by operational volcanic ash advisory centers. Thus, it is important to find ways to differentiate between airborne Asian dust and airborne volcanic ash. Use of Total Ozone Mapping Spectrometer aerosol and sulfur dioxide indices, in conjunction with the split-window method, can mitigate the possibility of a false airborne volcanic ash alarm. Asian dust also is important for other reasons. Thus, meteorological agencies should monitor it because 1) it can be transported thousands of kilometers from its source region and thus is of global interest (e.g., effects on radiative forcing) and 2) fine-grain particles pose a potentially serious public health hazard.