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Record W2972809368 · doi:10.1371/journal.pcbi.1007296

Ten simple rules for helping newcomers become contributors to open projects

2019· article· en· W2972809368 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLoS Computational Biology · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsTeldio (Canada)
Fundersnot available
KeywordsPublic relationsOpenness to experienceSociologyWork (physics)Knowledge managementComputer sciencePolitical sciencePsychologyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

To survive and thrive, a must attract new members, retain them, and help them be productive [1]. As openness becomes the norm in research, software development, and education, knowing how to do this has become an essential skill for principal investigators and managers alike. A growing body of knowledge in sociology, anthropology, education, and software engineering can guide decisions about how to facilitate this. What exactly do we mean by community? In the case of open source and open science, the most usual meaning is a community of practice. As defined by Lave and Wenger [2, 3], groups as diverse as knitting circles, oncology researchers, and web designers share three key characteristics: Participants have a common product or purpose that they work on or toward. They are mutually engaged, i.e., they assist and mentor each another. They develop shared resources and domain knowledge. Brown [4] specializes this to define a community of as …a formed in pursuit of a common goal. The goal can be definite or indefinite in time, and may not be clearly defined, but it is something that (generally speaking) the is aligned on. People working to preserve coral reefs in the face of global climate change are an example of such a community. No central organization coordinates their work, but the scientists who study coral reefs, the environmentalists who work to protect them, and the citizens who support them financially and politically are aware of each other’s efforts, collaborate in ad hoc ways, and are conscious of contributing toward a shared purpose. Open-source software projects are also communities of effort. E.g., the Mozilla Firefox [5] includes a mix of paid professionals, highly involved volunteers, and occasional contributors who not only create software, documentation, and tutorials but also organize events, answer questions in online forums, mentor newcomers, and advocate for open standards. Every of effort has unique features, but they have enough in common to profit from one another’s experience. The 10 rules laid out below are based on studies of such communities and on the authors’ experience as members, leaders, and observers. Our focus is on small and medium-sized projects, i.e., ones that have a handful of to a few hundred participants and are a few months to a few years old but may not (yet) have any formal legal standing, such as incorporation as a nonprofit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.443
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.326
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it