656 Steven W. Boydensured the statistical validity <strong>of</strong> the study. 42 Also to guarantee statisticalvalidity a necessary condition <strong>for</strong> the <strong>in</strong>clusion <strong>of</strong> a text was that thef<strong>in</strong>ite verb count not be too low. Thirty was the lower limit <strong>for</strong> the study.Consequently, short contiguous narratives were grouped together andpsalms were grouped by author (if <strong>in</strong>dicated <strong>in</strong> the superscription), type,collections, and so <strong>for</strong>th. Another th<strong>in</strong>g avoided was widely divergenttext sizes. Long texts, there<strong>for</strong>e, were subdivided <strong>in</strong>to smaller units.How can <strong>Biblical</strong> <strong>Hebrew</strong> narratives be identified? In addition towhat was said <strong>in</strong> Section 3.4 above, <strong>Biblical</strong> <strong>Hebrew</strong> narratives—justas narratives <strong>in</strong> any other literature—tell a story, with a sett<strong>in</strong>g, thetime and place <strong>in</strong> which the events <strong>of</strong> the story unfold; character(s),the person or persons who do and say th<strong>in</strong>gs <strong>in</strong> the story or have th<strong>in</strong>gsdone or said to them; and the plot. Plot <strong>in</strong>volves two movements. Thefirst is a movement from a situation <strong>in</strong> which the characters are <strong>in</strong>equilibrium with one another to one where the characters are no longer<strong>in</strong> equilibrium. They encounter a situation that places them <strong>in</strong> dangeror <strong>in</strong>troduces an unrealized expectation. This last situation is unstableand demands resolution. This resolution is the second movement:the characters move to a new equilibrium. L<strong>in</strong>guistically, narrativesare characterized by lexical transparency, normal syntax, and literallanguage.<strong>Biblical</strong> <strong>Hebrew</strong> poetry also is not difficult to identify. First <strong>of</strong> all (asdiscussed <strong>in</strong> Section 3.1 above), <strong>in</strong> the oldest complete manuscript <strong>of</strong>the <strong>Hebrew</strong> Bible, the Len<strong>in</strong>grad Codex B19a, a few texts are laid out asverse: the Song <strong>of</strong> the Sea, the Balaam Oracles, the Song and Bless<strong>in</strong>g<strong>of</strong> Moses, the Song <strong>of</strong> Deborah, the Song <strong>of</strong> David (2 Samuel 22), theF<strong>in</strong>al Words <strong>of</strong> David (2 Samuel 23) and Psalms 119 and 136. Second,poetry has a different poetics from narrative [see Sections 3.2 and 3.3above]. Third, poetry differs from narrative <strong>in</strong> its <strong>in</strong>tended effects.The <strong>Biblical</strong> poet wants his readers to see, hear, smell, feel, and tastewhat he is experienc<strong>in</strong>g. He is seek<strong>in</strong>g to evoke his readers’ emotionsand experiences. He does this by creat<strong>in</strong>g vivid un<strong>for</strong>gettable images,which resonate with his readers.By apply<strong>in</strong>g the criteria discussed above the two populations wereidentified—295 narrative texts and 227 poetic texts.
Evidence <strong>for</strong> an Historical Read<strong>in</strong>g <strong>of</strong> Genesis 1:1–2:3 6575.2 The Null HypothesisTo determ<strong>in</strong>e the <strong>in</strong>ferential potential <strong>of</strong> the ratio <strong>of</strong> preterites to f<strong>in</strong>iteverbs the null hypothesis H 0and the alternative H 1were put <strong>for</strong>th asfollows:H 0: All logistic regression classification models, <strong>in</strong> which the relativefrequency <strong>of</strong> preterites is the only <strong>in</strong>dependent variable, classify textsaccord<strong>in</strong>g to genre no better than random classification.This is tested aga<strong>in</strong>st the alternative hypothesis:H 1: There is a classification model that classifies texts by genre betterthan random classification.5.3 The Random SampleIn order to ensure that texts from all periods and from all three parts<strong>of</strong> the <strong>Hebrew</strong> Bible (Torah, Prophets, and Writ<strong>in</strong>gs) were <strong>in</strong>cluded<strong>in</strong> the analysis, a stratified random sample was generated. Also, thissample <strong>in</strong>cluded extra texts to replace any primary texts rejected <strong>for</strong>violat<strong>in</strong>g any <strong>of</strong> the conditions stated above.The breakdown <strong>of</strong> the narrative text <strong>in</strong> the sample is: Torah (15 out <strong>of</strong>87, 2 extra); Former Prophets (21 out <strong>of</strong> 138, 3 extra); Latter Prophets (2out <strong>of</strong> 11, 2 extra); and Writ<strong>in</strong>gs (10 out <strong>of</strong> 59, 2 extra). The breakdown<strong>of</strong> the poetic texts <strong>in</strong> the sample is: Torah (3 out <strong>of</strong> 13, 2 extra); FormerProphets (3 out <strong>of</strong> 12, 2 extra); Latter Prophets (23 out <strong>of</strong> 104, 3 extra);and Writ<strong>in</strong>gs (22 out <strong>of</strong> 98, 3 extra).5.4 Visualiz<strong>in</strong>g the DataAga<strong>in</strong> BibleWorks 5.0 was used to count the verbs <strong>for</strong> each text. Thedata is tabulated <strong>in</strong> Appendix B <strong>in</strong> Tables B1–B8.3-D bar graphs and scatter plots visually present the data. 3-D bargraphs display the relative frequency <strong>of</strong> f<strong>in</strong>ite verbs. For the 3-D bargraphs (Figures 4–5) the colors <strong>for</strong> the relative frequencies <strong>of</strong> preterites,imperfects, perfects and waw-perfects are green, red, yellow, and blue,respectively (go<strong>in</strong>g back <strong>in</strong>to the page on the y-axes). The passages
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