Let $ν_1,ν_2,\dots$ be a sequence of probabilities on the nonnegative integers, and $X=(X_1,X_2, \dots)$ be a sequence of independent random variables $X_i$ with law $ν_i$. For $λ>0$ denote $Z^λ_i:= \sum_x λ^xν_i(x)$ and $λ^{\max}:= \sup\{λ>0: Z^λ_i<\infty \text{ for all }i\}$, and assume $λ^{\max}>1$. For $λ<λ^{\max}$, define the tilted probability $ν_i^λ(x):= λ^xν_i(x)/Z^λ_i$, and let $X^λ$ be a sequence of independent variables $X^λ_i$ with law $ν^λ_i$, and denote $S^λ_n:= X^λ_1+\dots+X^λ_n$, with $S_n=S^1_n$. Choose $λ^*\in(1,λ^{\max})$ and denote $R^*_n:= E (S^{λ^*}_n)$. The Gibbs Conditioning Principle (GCP) holds if $P(X\in\cdot|S_n>R^*_n)$ converges weakly to the law of $X^{λ^*}$, as $n\to\infty$. We prove the GCP for log-concave $ν_i$'s, meaning $ν_i(x+1)\,ν_i(x-1) \le ( ν_i(x))^2$, subject to a technical condition that prevents condensation. The canonical measures are the distributions of the first $n$ variables, conditioned on their sum being $k$. Efron's theorem states that for log-concave $ν_i$'s, the canonical measures are stochastically ordered with respect to $k$. This, in turn, leads to the ordering of the conditioned tilted measures $P(X^λ\in\cdot|S^λ_n>R^*_n)$ in terms of $λ$. This ordering is a fundamental component of our proof.