Let $(X,\mathcal{B},P)$ be a probability space and $\mathit{a}$ be a sub $σ$-field that is generated by an increasing sequence of sub $σ$-fields $(\mathit{a}_{n})_{n \in \mathbb{N}}$. Given $θ\in Θ$, where $Θ$ is some set, let $(X_{n}^θ)_{n \in \mathbb{N}}$ be a martingale adapted to $(\mathit{a}_{n})_{n \in \mathbb{N}}$. Martin (1969) provides sufficient conditions to show that $(X_{n}^θ)_{n \in \mathbb{N}}$ converges a.s. uniformly on $Θ$ to a random variable $X^θ$. His results are based on the assumption that there exists an integer $n$ s.t. the conditional entropy given $\mathit{a}_{n}$ is uniformly bounded over the set of finite partitions of $X$ with atoms from $\mathit{a}$. This study complements Martin's results by studying the latter assumption on the maximal conditional entropy in the context of measurable partitions of Lebesgue spaces. We provide conditions under which $\mathit{a}$ conveys too much information for the maximal conditional entropy to be finite. As an example, we consider the space of continuous functions with a compact support, equipped with the Borel $σ$-field.