Randomness versus specifics for word-frequency distributions

The text-length-dependence of real word-frequency distributions can be connected to the general properties of a random book. It is pointed out that this finding has strong implications, when deciding between two conceptually different views on word-frequency distributions, i.e. the specific `Zipf's-view' and the non-specific `Randomness-view', as is discussed. It is also noticed that the text-length transformation of a random book does have an exact scaling property precisely for the power-law index $\gamma=1$, as opposed to the Zipf's exponent $\gamma=2$ and the implication of this exact scaling property is discussed. However a real text has $\gamma>1$ and as a consequence $\gamma$ increases when shortening a real text. The connections to the predictions from the RGF(Random Group Formation) and to the infinite length-limit of a meta-book are also discussed. The difference between `curve-fitting' and `predicting' word-frequency distributions is stressed. It is pointed out that the question of randomness versus specifics for the distribution of outcomes in case of sufficiently complex systems has a much wider relevance than just the word-frequency example analyzed in the present work.