A Toolkit for Measuring the Impacts of Public Funding on Open Source Software Development
- URL: http://arxiv.org/abs/2411.06027v1
- Date: Sat, 09 Nov 2024 01:13:45 GMT
- Title: A Toolkit for Measuring the Impacts of Public Funding on Open Source Software Development
- Authors: Cailean Osborne, Paul Sharratt, Dawn Foster, Mirko Boehm,
- Abstract summary: Impacts of public funding on open source software development remain poorly understood.
We present a taxonomy of potential social, economic, and technological impacts that can be both positive and negative.
With this toolkit, we contribute to the multi-stakeholder conversation about the value and impacts of funding on OSS developers and society at large.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Governments are increasingly employing funding for open source software (OSS) development as a policy lever to support the security of software supply chains, digital sovereignty, economic growth, and national competitiveness in science and innovation, among others. However, the impacts of public funding on OSS development remain poorly understood, with a lack of consensus on how to meaningfully measure them. This gap hampers assessments of the return on public investment and impedes the optimisation of public-interest funding strategies. We address this gap with a toolkit of methodological considerations that may inform such measurements, drawing on prior work on OSS valuations and community health metrics by the Community Health Analytics Open Source Software (CHAOSS) project as well as our first-hand learnings as practitioners tasked with evaluating funding programmes by the Next Generation Internet initiative and the Sovereign Tech Agency. We discuss salient considerations, including the importance of accounting for funding objectives, project life stage and social structure, and regional and organisational cost factors. Next, we present a taxonomy of potential social, economic, and technological impacts that can be both positive and negative, direct and indirect, internal (i.e. within a project) and external (i.e. among a project's ecosystem of dependents and users), and manifest over various time horizons. Furthermore, we discuss the merits and limitations of qualitative, quantitative, and mixed-methods approaches, as well as options for and hazards of estimating multiplier effects. With this toolkit, we contribute to the multi-stakeholder conversation about the value and impacts of funding on OSS developers and society at large.
Related papers
- An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)
This paper explores potential areas where statisticians can make important contributions to the development of LLMs.
We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Ten Challenging Problems in Federated Foundation Models [55.343738234307544]
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning.
This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency.
arXiv Detail & Related papers (2025-02-14T04:01:15Z) - Cracking the Code: Enhancing Development finance understanding with artificial intelligence [0.0]
This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), and an innovative Python topic modeling technique called BERTopic.
By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding.
arXiv Detail & Related papers (2025-02-13T17:01:45Z) - An Overview of Cyber Security Funding for Open Source Software [3.5880059456896842]
The paper examines two such funding bodies for OSS and the projects they have funded.
The focus of both funding bodies is on software security and cyber security in general.
arXiv Detail & Related papers (2024-12-08T10:48:30Z) - The Role of Community Building and Education as Key Pillar of Institutionalizing Responsible Quantum [0.9822850913898894]
Quantum computing is an emerging technology whose positive and negative impacts on society are not yet fully known.
Government, individuals, institutions, and corporations must ensure that they anticipate its impacts, prepare for its consequences, and steer its development in such a way that it enables the most good and prevents the most harm.
This paper reviews responsible quantum computing proposals and literature, highlights the challenges in implementing these, and presents strategies developed at IBM aimed at building a diverse community of users and stakeholders to support the responsible development of this technology.
arXiv Detail & Related papers (2024-10-17T20:34:40Z) - Affective Computing Has Changed: The Foundation Model Disruption [47.88090382507161]
We aim to raise awareness of the power of Foundation Models in the field of Affective Computing.
We synthetically generate and analyse multimodal affective data, focusing on vision, linguistics, and speech (acoustics)
We discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area.
arXiv Detail & Related papers (2024-09-13T15:20:18Z) - The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications.
To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
arXiv Detail & Related papers (2024-06-24T15:55:49Z) - A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain.
We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation.
We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
arXiv Detail & Related papers (2024-06-15T16:11:35Z) - Public-private funding models in open source software development: A case study on scikit-learn [0.0]
This study is a case study on scikit-learn, a Python library for machine learning funded by public research grants, commercial sponsorship, micro-donations, and a 32 euro million grant announced in France's artificial intelligence strategy.
Through 25 interviews with scikit-learn's maintainers and funders, this study makes two key contributions.
It contributes empirical findings about the benefits and drawbacks of public and private funding in an impactful OSS project, and the governance protocols employed by the maintainers to balance the diverse interests of their community and funders.
arXiv Detail & Related papers (2024-04-09T17:35:11Z) - Towards a Critical Open-Source Software Database [0.0]
CrOSSD project aims to build a database of OSS projects and measure their current project "health" status.
quantitative metrics will be gathered through automated crawling of meta information such as the number of contributors, commits and lines of code.
qualitative metrics will be gathered for selected "critical" projects through manual analysis and automated tools.
arXiv Detail & Related papers (2023-05-02T10:43:21Z) - Understanding Information Disclosure from Secure Computation Output: A Study of Average Salary Computation [58.74407460023331]
Quantifying information disclosure about private inputs from observing a function outcome is the subject of this work.
Motivated by the City of Boston gender pay gap studies, in this work we focus on the computation of the average of salaries.
arXiv Detail & Related papers (2022-09-21T15:59:48Z) - Seeing poverty from space, how much can it be tuned? [0.0]
We demonstrate that individuals with no organizational affiliation can participate in the improvement of predicting local poverty levels in a given agro-ecological environment.
The approach builds upon several pioneering efforts related to mapping poverty by deep learning to process satellite imagery and "ground-truth" data from the field.
A key goal of the project was to intentionally keep costs as low as possible - by using freely available resources - so that citizen scientists, students and organizations could replicate the method in other areas of interest.
arXiv Detail & Related papers (2021-07-30T15:23:54Z) - Estimating Fund-Raising Performance for Start-up Projects from a Market
Graph Perspective [58.353799280109904]
We propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
Specifically, we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
arXiv Detail & Related papers (2021-05-27T02:39:30Z) - Forecasting for Social Good [0.8295385180806493]
We present some key attributes that qualify a forecasting process as Forecasting for Social Good (FSG)
FSG is concerned with advancing social and environmental goals and prioritises these over conventional measures of economic success.
We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area.
arXiv Detail & Related papers (2020-09-24T13:16:57Z) - Leveraging traditional ecological knowledge in ecosystem restoration
projects utilizing machine learning [77.34726150561087]
Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being.
We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
arXiv Detail & Related papers (2020-06-22T16:17:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.