A game for the first year of a PhD. You make the thirteen decisions a study is made of; each one names the pitfall it risks, what it costs, and the fix. Then you judge other people's plans and see whether your confidence matches your accuracy: that is taste, and it is trainable.
The pitfalls are universal; the wording isn't. Machine learning is the main focus; the other packs re-voice the same traps and add a few of their own.
Runs in your browser; nothing is sent anywhere. Progress stays on this device. It is a game, not a checklist. Dotted terms have a one-line explanation on hover; every card also lists its terms underneath, tap-friendly.
The outcome is what you're judged on; the process is your biggest lever.
Rigor after each decision. Color = the call you made. Hover a dot for the card.
Open one to see what it is, how to notice it next time, and what to read.
Each pair is two versions of the same kind of work that differ in one feature; the reveal names the feature and the principle behind it.
Taste is the judging half of a GAN (the discriminator): it gets sharper by judging many examples and being told the answer.
Each dot: how often you were right when you said that confidence. On the diagonal = calibrated. Below = overconfident.
The thing that separated strong from weak. Open one for the argument.
Five questions a supervisor, a reviewer and a funder all ask, with anchored scales. Score a sample idea first and compare with the student who proposed it and two reviewers, or score your own and get a one-paragraph scope statement.
Sample ideas for your domain first, then the others.
Every pitfall in the game, by phase. Open a tile for what it is, what it smells like, and the fix. Dots show what your runs on this device hit (red) or walked past (green).
Terms the game uses, in one line each.
Everything linked in the game, in one place. P2P is The Path to PhD, the newsletter this game grew out of.