scholarly journals Challenges and Resilience-Building: A Narrative Inquiry Study on a Mid-Career Chinese EFL Teacher

2021 ◽  
Vol 12 ◽  
Author(s):  
Lina Xue
Keyword(s):  
Narrative Inquiry ◽  
High Rate ◽  
Female Teacher ◽  
Experienced Teacher ◽  
Soft Data ◽  
Chinese Context ◽  
Teacher Resilience ◽  
Hard Data ◽  
Efl Teacher ◽  
Reflective Study

With a high rate of attrition and burnout of teachers as a global concern, teacher resilience has become a trendy topic in the research of their professional development as one of the pillars of positive psychology (positive character traits). However, the literature reveals that little research has been done on the mid-career teachers in the Chinese context, especially on how resilience may be nurtured, sustained, or eroded over time. Focusing on a mid-career EFL female teacher (the author) in China as a case study, this longitudinal self-reflective study employs a narrative inquiry to investigate the challenges that the experienced teacher was encountered with and to depict her trajectories of resilience-building by fleshing out the interaction between challenges, resources, and coping strategies in her three different scenarios. “Hard data,” such as teaching journals, reflective field notes, and messages with students were collected and analyzed inductively by using thematic analysis, and “soft data,” like memory was also referred to. The findings unfolded challenges confronting the experienced teacher peculiar to the Chinese context and charted a detailed bumpy journey of resilience building in three phases, accompanied by her growing emotional, intellectual, and psychological capacities. Implications are drawn out for teacher resilience building, school leaders, and policymakers.

Greatly Exaggerated in Canada: Diverging Data and Media Bailouts

2021 ◽  
Vol 46 (3) ◽  
Author(s):  
Marc W Edge
Keyword(s):  
News Media ◽  
Research Centre ◽  
Policy Outcomes ◽  
Think Tank ◽  
Soft Data ◽  
Hard Data ◽  
Media Research ◽  
Using Data ◽  
Media Narratives ◽  
Financial Losses

Background: The Canadian government allocated $595 million in subsidies over five years to news media in 2019, but the bailout was based on questionable data. Financial losses were exaggerated; a think tank report was criticized for using data selectively; data from a university research project differed sharply from annual industry counts; and job loss figures were disputed. Analysis: Hard data can diverge markedly from soft data accepted in pursuit of policy outcomes. Conclusions and implications: A second campaign underway on behalf of entertainment industries could yield a bailout several times larger than the first. Closer scrutiny should be exercised of media narratives and offered data. An independent media research centre should collect and verify data for policy purposes.Contexte : En 2019, le gouvernement canadien a octroyé aux médias d’information 595 millions de dollars en subventions étalées sur cinq ans, un montant évalué à partir de données douteuses. En effet, on a surestimé les pertes financières dans le milieu; le rapport influent d’un groupe de réflexion se fondait sur des données sélectionnées pour les besoins de la cause; les données provenant d’un projet de recherche universitaire différaient beaucoup de celles fournies annuellement par l’industrie; et on a exagéré les pertes d’emploi. Analyse : Les données dures peuvent différer énormément des données molles acceptées dans le but d’atteindre certains objectifs politiques. Conclusion et implications : Une seconde campagne menée pour aider les industries du divertissement pourrait bénéficier de subventions encore plus généreuses que les premières. Avant de procéder, il serait judicieux d’examiner de près les narratifs des médias et les données proposées. À cet égard, on devrait créer un centre indépendant pour la recherche sur les médias qui pourrait lui même recueillir et vérifier les données utilisées pour formuler des politiques.

scholarly journals Static Geological Modelling with Knowledge Driven Methodology

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3802
Author(s):  
Jun Li ◽  
Xiaoying Zhang ◽  
Bin Lu ◽  
Raheel Ahmed ◽  
Qian Zhang
Keyword(s):  
Case Studies ◽  
Oil And Gas ◽  
Fracture Network ◽  
Depositional Environments ◽  
Soft Data ◽  
Oil And Gas Exploration ◽  
Hard Data ◽  
The North ◽  
Geological Modelling ◽  
Exploration And Production

Geological modelling is an important topic of oil and gas exploration and production. A new knowledge driven methodology of geological modelling is proposed to address the problem of “hard data” limitation and modelling efficiency of the conventional data driven methodology. Accordingly, a new geological modelling software (DMatlas) (V1.0, Dimue, Wuhan, China) has been developed adopting a grid-free, object-based methodology. Conceptual facies models can be created for various depositional environments (such as fluvial, delta and carbonates). The models can be built largely based on geologists’ understandings with “soft data” such as outcrops analysis and geological maps from public literatures. Basic structures (fault, folds, and discrete fracture network) can be easily constructed according to their main features. In this methodology, models can be shared and re-used by other modelers or projects. Large number of model templates help to improve the modelling work efficiency. To demonstrate the tool, two case studies of geological modelling with knowledge driven methodology are introduced: (1) Suizhong 36-1 field which is a delta depositional environment in Bohai basin, China; (2) a site of the north Oman fracture system. The case studies show the efficiency and reliability within the new methodology.

Representation of Soft and Hard Data for Urban Areas—Case Studies in Berlin and Karlsruhe

1981 ◽  
Vol 13 (12) ◽  
pp. 1563-1575
Author(s):  
H F Kern
Keyword(s):  
Data Acquisition ◽  
Case Studies ◽  
Urban Areas ◽  
Soft Data ◽  
Hard Data ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Computer Cartography

This paper examines the utility of hard and soft data in computer cartography, with respect both to the method of data acquisition and to the type of information recorded. The potential and problems of linking computer aided analysis with automated cartographic representation are considered. By way of illustrative examples, the objectives of two case studies in Berlin and Karlsruhe are stated and some of the relevant cartographical results reproduced.

Hard data, soft data

BMJ ◽  
2012 ◽  
Vol 344 (mar28 1) ◽  
pp. e1692-e1692
Author(s):  
J. Aronson
Keyword(s):  
Soft Data ◽  
Hard Data

scholarly journals Modeling and Simulation of Project Management through the PMBOK® Standard Using Complex Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Luz Stella Cardona-Meza ◽  
Gerard Olivar-Tost
Keyword(s):  
Project Management ◽  
Dynamic Structure ◽  
Performance Outcomes ◽  
Complex Environments ◽  
Soft Data ◽  
Academic Literature ◽  
Hard Data ◽  
Process Dynamics ◽  
Data Process ◽  
Dynamic Components

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, and a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data (process dynamics) and soft data (executive team dynamics). In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects.

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