What does this equation mean?

Everything above is a case for treating the architectural account as at least as well supported as the psychological framing. It is not, on its own, a decisive discriminator, and this article’s one genuine contribution is naming and specifying the measurement that would be. Call it the sink-attributable fraction, $\phi_{\mathrm{sink}}$ , defined as $\phi_{\mathrm{sink}} = 1 - \frac{\mathrm{SPEM}_{\mathrm{sigmoid}}}{\mathrm{SPEM}_{\mathrm{softmax}}}$. where $\mathrm{SPEM}_{\mathrm{softmax}}$ is Guo and Vosoughi’s own Jensen–Shannon-divergence effect magnitude, measured by their own published protocol, on an ordinary sink-forming model, and $\mathrm{SPEM}_{\mathrm{sigmoid}}$ is the identical measurement on an architecture- and data-matched twin trained with sigmoid attention instead of softmax — the exact contrast Gu and colleagues’ released training pipeline was built to produce [ 3 ] . The quantity is dimensionless, bounded in the neighborhood of zero to one for any model pair where the sink-free twin is not somehow more position-sensitive than its sink-forming counterpart, and it answers a single, sharp question: of the effect magnitude the original paper measured, how much of it disappears when the one specific architectural mechanism this article names is removed and nothing else changes?