Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

Intentional Innovation: How Getting More Systematic About Innovation Could Improve Philanthropy and Increase Social Impact

Based on a review of case studies and current innovation theory and practice, proposes a framework integrating best practices, processes, and tools for making innovation a more consistent and integral element of philanthropy. Lists models and resources

What's Next for Community Philanthropy: Making the Case for Change

The models of community foundations today vary almost as widely as the communities in which they're based. While many organizations remain focused on traditional activities like endowment management, donor service, and grantmaking, other community foundations have begun to experiment with new opportunities for serving their communities, from financing social impact bonds to facilitating community dialogue.Yet despite a growing record of innovation, the prevailing narrative of the community foundation field has remained largely unchanged as the model hits its centennial anniversary. Instead of a story of adaptation and diversity, the field is still viewed as if it had a single, uniform model -- acting as a charitable bank for their communities -- that no longer really represents the heart of what many community foundations do.This dated narrative is beginning to hold community foundations back. It prevents outsiders from seeing the vibrancy and innovation going on in the field, and it pushes many community philanthropy organizations to retrench defensively in the face of new competitive challenges at a time when they would be better off opening themselves up to new ideas and new ways of serving their communities.The Monitor Institute's What's Next for Community Philanthropy initiative aims to shift this narrative, and to help the community foundation field enter its second century on its front foot. The complete toolkit can be found here: http://monitorinstitute.com/communityphilanthropy/toolkit

On the Brink of the New Promise: The Future of US Community Foundations

This report provides a synthesis of the changing environment for community philanthropy and its implications for community foundations

What's Next for Philanthropy: Acting Bigger and Adapting Better in a Networked World (Snapshot)

Where the cutting edge of philanthropic innovation over the last decade was mostly about improving organizational effectiveness, efficiency, and responsiveness, we believe that the next practices of the coming 10 years will have to build on those efforts to include an additional focus on coordination and adaptation. The most innovative funders in the future will do more than operate as effective, independent institutions.This two-page overview of the report and links to the full suite of materials; complete report, executive summary, and an innovation toolkit

What's Next for Philanthropy: Acting Bigger and Adapting Better in a Networked World (Executive Summary)

Our final report highlights how changes in the world around philanthropy will call on funders to not only adopt today's best practices, but also to pioneer "next practices"—effective approaches that are well-suited to tomorrow's more networked, dynamic, and interdependent landscape of public problem solving. It details 10 specific next practices that we believe will help funders have greater impact on growing social and environmental problems

Reimagining measurement: Enhancing social impact through better monitoring, evaluation, and learning

Social sector organizations tackle some of the world's most difficult and complex challenges on a daily basis. And, just as in other industries, getting the right data and information at the right time is essential to understanding what an organization needs to achieve, whether it is doing what it set out to do, and what impact its efforts are actually having. Yet, despite marked advances in the tools and methods for monitoring, evaluation, and learning in the social sector, as well as a growing number of bright spots in practice emerging in the field, there is broad dissatisfaction across the sector about how data is -- or is not -- used.Three straightforward principles -- purpose, perspective, and alignment with other actors -- can help the social sector reinvent its approach to measuring social impact, turning data into an asset that benefits both philanthropic organizations and those they seek to help

Making the Case for Diversity in Philanthropy

While many foundations have long sought to become more inclusive as a logical extension of their missions, the business case for incorporating diversity has renewed interest in understanding how diversity can also enhance the effectiveness of philanthropic organizations. Because prior movements to increase diversity in philanthropy have focused on moral, rather than operational, arguments and because the field lacks an easy way to measure outcomes, strong evaluation measures of diversity's impacts on philanthropic activity have not been developed. Numerous studies from the corporate sphere, however, suggest that greater inclusiveness may improve an organization's processes and outcomes. Many philanthropic experts and practitioners believe that these gains may apply to grantmaking institutions as well as businesses.

Indium extraction from LCD screens

Liquid crystal display (LCD) screens are present in a variety of electronic devices including televisions, computers, cell phones, global positioning system (GPS) devices, and others. On a vitreous layer of their inner surface these screens contain the chemical element indium. The presence of this element, considered a critical raw material due to its economic importance and scarce availability, renders the recycling of these screens increasingly attractive. The present study therefore was undertaken with the aim of extracting indium present in LCD screens. Damaged or obsolete monitors with LCD screens were collected and dismantled manually to remove the glass layer containing indium, and subsequently, the glass layer was ground in a ball mill. After grinding, leaching tests for indium extraction were performed. Hydrochloric acid (HCl), at different temperatures and concentrations, was tested as a leaching agent at solid/liquid ratios of 1/100 and 1/10. The results obtained reveal the possibility of extracting indium, with the best result being obtained with HCl 6 M, 60°C, s/l ratio 1/100, with 298 mg In/kg

Frivolous Units: Wider Networks Are Not Really That Wide

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests that developing compressible representations is key for adjusting the complexity of large networks to the learning task at hand. However, these compressible representations are poorly understood. A promising strand of research inspired from biology is understanding representations at the unit level as it offers a more granular and intuitive interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity as their width is increased? If so, how do these depend on the architecture, dataset, and training parameters? We identify two distinct types of "frivolous" units that proliferate when the network's width is increased: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network represents could be expressed by a network without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs