In frequentist statistics, the true population parameter is fixed, not random, so the claim "the relative risk will be less 0.65 2.5% of the time" has no meaning. The 95% claim is that if we repeatedly sample new data, 95% of the time we will generate a confidence interval covering the true parameter; but that does not say anything about the individual confidence interval in this specific case, about which we have no probability claim. Indeed, it's not clear what a 95% chance would mean for a specific interval, since probability in frequentist statistics is defined in terms of long-run probabilities over repeated samples of data.

But the impulse to interpret confidence intervals in a Bayesian way is common, since it's the question we want confidence intervals to answer. You can find a lot of academic papers arguing about this as a sign of the inadequacy of frequentist statistics.