Reading, writing and apprenticeships: developing an authentic reading and assessment strategy for graduate apprenticeships.

The recent launch of graduate apprenticeships in Scotland requires participating universities to collaborate closely with employers to design and develop innovative curricula to enable apprentices to acquire new knowledge and develop relevant skills both in a traditional university learning and teaching setting as well as in the workplace. This paper argues that the additional context of learning situated in the workplace provides a particular impetus to consider and reflect the requirement and deployment of authentic reading strategies and authentic assessment regimes as essential design elements in these programmes. We present a discussion of the approach we are adopting to designing curricula and preparing learning and assessment resources for graduate apprenticeships in Business Management and Business Management: Financial Services. We focus here specifically on our plans for helping apprentices on these programmes to navigate the plethora of information resources available to them and develop effective reading strategies and information literacy skills in both academic and professional contexts. We indicate how the enhancement of these skills forms an important precursor to tackling the authentic assessments designed for apprentices to evidence their professional and academic learning during their apprenticeships. Our planning and design activity draws first on aspects of our recent research into reading skills and strategies among professionals and business students, as well as on our established track record of delivering a variety of work-based learning programmes. It is envisaged that findings and lessons learned from our work will help guide and inform other institutions across the UK as they establish curricula for graduate or degree apprenticeships

The data-index: An author-level metric that values impactful data and incentivizes data sharing.

Author-level metrics are a widely used measure of scientific success. The h-index and its variants measure publication output (number of publications) and research impact (number of citations). They are often used to influence decisions, such as allocating funding or jobs. Here, we argue that the emphasis on publication output and impact hinders scientific progress in the fields of ecology and evolution because it disincentivizes two fundamental practices: generating impactful (and therefore often long-term) datasets and sharing data. We describe a new author-level metric, the data-index, which values both dataset output (number of datasets) and impact (number of data-index citations), so promotes generating and sharing data as a result. We discuss how it could be implemented and provide user guidelines. The data-index is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that both for the benefit of scientific progress and to create a value system that is more equitable, diverse, and inclusive. Future work should focus on promoting other scientific contributions, such as communicating science, informing policy, mentoring other scientists, and providing open-access code and tools

A fresh approach to evidence synthesis.

For most conservation interventions, insufficient evidence exists for investigators to be able to conduct a meta-analysis for each species or genus. However, by being apprised of studies that examine how a particular intervention has worked for an order — for birds in general, say — practitioners can better weigh up the chances of success for their intended programme

Quantifying the impact and relevance of scientific research

Qualitative and quantitative methods are being developed to measure the impacts of research on society, but they suffer from serious drawbacks associated with linking a piece of research to its subsequent impacts. We have developed a method to derive impact scores for individual research publications according to their contribution to answering questions of quantified importance to end users of research. To demonstrate the approach, here we evaluate the impacts of research into means of conserving wild bee populations in the UK. For published papers, there is a weak positive correlation between our impact score and the impact factor of the journal. The process identifies publications that provide high quality evidence relating to issues of strong concern. It can also be used to set future research agendas

Beware greedy algorithms

Nestedness – the tendency for specialist species to interact with subsets of the species that generalist species interact with – is a pervasive feature of empirical mutualistic communities (Bascompte, Jordano, Melián, & Olesen, 2003). While theoretical work has discovered important dynamical implications of nestedness, such as enhanced community stability and species coexistence (Bastolla et al., 2009; Rohr, Saavedra, & Bascompte, 2014; Thébault & Fontaine, 2010), there has been less agreement about why networks vary in their levels of nestedness. Answering this question is an important challenge as it has the potential to improve understanding of the mechanisms leading to nested architectures and hence the processes underlying community persistence.Arcadia Cambridge Faculty of Mathematics CMP bursary fund Natural Environment Research Council as part of the Cambridge Earth System Science NERC DTP. Grant Number: NE/L002507/

Organising evidence for environmental management decisions: a '4S' hierarchy.

Making decisions informed by the best-available science is an objective for many organisations managing the environment or natural resources. Yet, available science is still not widely used in environmental policy and practice. We describe a '4S' hierarchy for organising relevant science to inform decisions. This hierarchy has already revolutionised clinical practice. It is beginning to emerge for environmental management, although all four levels need substantial development before environmental decision-makers can reliably and efficiently find the evidence they need. We expose common bypass routes that currently lead to poor or biased representation of scientific knowledge. We argue that the least developed level of the hierarchy is that closest to decision-makers, placing synthesised scientific knowledge into environmental decision support systems.L.V.D. is funded by the Natural Environment Research Council (Grant code NE/K015419/1). J.C.W. is funded by the UK Commonwealth Scholarship Commission and the Cambridge Commonwealth, European and International Trust. W.J.S. is funded by Arcadia.This is the final version of the article. It first appeared from Cell Press/Elsevier via http://dx.doi.org/10.1016/j.tree.2014.09.00

Correspondence with the U. S. Legislature, Smoot, and Sutherland

Papers involving a correspondence with the U.S. Legislature, Smoot, and Sutherland