Academic reference on the drawbacks of accuracy, F1 score, sensitivity and/or specificity Accuracy, as a KPI for assessing binary classification models, has major drawbacks: Why is accuracy not the best measure for assessing classification models?. The exact same issues also plague the F1 score (actually all Fβ scores), sensitivity, specificity and alternatives.
Is there a standard academic article one can point to discussing these issues?
Why am I asking this? I am thinking of reviewing a paper and wanting the author to avoid these KPIs. Or alternatively, having submitted a paper, getting reviews that recommend these flawed KPIs, and needing a paper to point to in arguing why I won't follow these recommendations. Of course, I could point to the CV thread linked above, but unfortunately, CV is not always accorded the respect a peer-reviewed article gets.
I have looked through Frank Harrell's "Damage Caused by Classification Accuracy and Other Discontinuous Improper Accuracy Scoring Rules". This kind of material is exactly what I am envisaging. Is there something like this published somewhere?

The main one that springs to mind is "Three myths about risk thresholds for prediction models" by Wynants et al. (2019) where they argue strongly against using a "universally optimal threshold" without context. I liked they used the term "dichotomania" too (in effect meaning: "manically dichotomising continuous variables").
I like Peter Flach's work on the area of "evaluating ML model performance" too. I do not have a single definitive reference there but something like Berrar's and his: "Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)" (2012) is a reasonable point to start. His "Precision-recall-gain curves: PR analysis done right"  (2015) with Kull has been very thought-provoking too.
