Tag: open-scholarship
10 items with this tag.
A personal design system for scholarly output
Almost everything I produce starts as a markdown file and ends in a different format — a slide deck, a handout, a Word document, this website — and until September 2026 I designed each one from scratch. I've replaced that with a personal design system with one visual language, one identity, and a template for each format, all reading the same token file. This post explains what the system is, how the pieces fit together technically, and what's still unfinished.
What the Quartz graph view reveals about this site
The graph view is one of the reasons I built this site with Quartz, and now that there's enough writing on it the links between pages have started to form a shape. The graph opens from the top bar, shows the site's writing as a network coloured by type, and lets you choose what's on it. This post shows how to read it, and what it reveals about the site: where the ideas cluster, which concepts hold the rest together, and which ones nothing connects to.
GitHub
GitHub is a website that hosts git repositories online and adds the tools people need to work on them together, including issues for discussion, pull requests for proposing and reviewing changes, and automation that can publish a project as a website. It was built for software developers, but anything kept as text files can live there, including teaching materials and scholarship.
Distributed version control
Distributed version control tracks changes to files so that every contributor holds a complete copy of the project and its full history, with no central server to depend on. It allows offline work, parallel development, and recovery from any copy, and it lets partners who share a document, such as a practice assessment document, each keep the whole record of how it changed.
Git
Git is a distributed version control system that tracks changes to files over time. It records who changed what and when, allows you to move between earlier and later states of a project, and lets multiple people work on the same files without overwriting each other's contributions.
Research industrial complex
The research industrial complex describes the self-reinforcing system of incentives across universities, funding bodies, journals, and publishers that rewards publication volume and impact metrics over meaningful scientific progress. The term draws on Eisenhower's military-industrial complex to show how interconnected institutional interests can sustain a system that works against its own stated mission.
An open scholarship workflow: What academic publishing can learn from open source
Academic publishing treats scholarship as a finished, individually owned artefact. This post describes a writing and publishing workflow built on a different premise: that a scholarly corpus could work like an open source project — readable, contributable, forkable, and never permanently owned by anyone.
A new home for my writing
Introducing my new platform for writing on AI in health professions education. It explains what's happened to usr/space and Head Space, what this newsletter will cover going forward, and links to my most recent presentations and posts.
Open access licensing
Open licences, mostly Creative Commons, state in advance what others may do with a work — read it, share it, adapt it, build on it — so nobody has to ask. The Creative Commons licence on teaching materials decides whether a colleague can adapt a resource for their own programme or has to rebuild it from scratch.
From teaching to learning: Rethinking education for a world of information abundance
Traditional education systems are structured around teaching, assuming it inevitably produces learning. This essay argues that this unidirectional model — knowledge flowing from expert to novice — no longer suits a world of information abundance and AI disruption. A networked, learner-centred approach offers an alternative, reconceptualising learning as a complex process where educators shift from knowledge authority to learning facilitator, enabling diverse participants to contribute to collective understanding. This transformation is urgent as AI develops capabilities once exclusive to human experts, and requires reimagining institutional structures around learning networks rather than teaching hierarchies to prepare graduates for complexity and uncertainty.