Synthesis

Art, Science, Morality

Let’s pick a leaf with regard to the “products” of academic research: Papers with embedded figures vs. Apps embedded in a Jupyter Book? Static vs. Dynamic? Final vs. Iterative? Nietzsche’s aphoristic style reflects his philosophy perfectly—disjointed, sharp, and meant to provoke rather than resolve. It’s fitting for someone who rejects linear narratives and grand teleological conclusions, as you pointed out with Marx and Dostoevsky. His idea of eternal recurrence, that life endlessly repeats without progress or redemption, clashes with the arcs that Dostoevsky’s tortured characters or Marx’s historical materialism offer. Those two are deeply invested in narrative arcs: Dostoevsky in the psychological and spiritual, Marx in the socio-political. Nietzsche, by contrast, thrives in fragmentation and repetition—the absence of a clean resolution is the point. It mirrors life’s chaotic, non-linear nature.

He’s like a philosopher who enjoys the struggle but isn’t interested in wrapping it up with a bow. His style keeps the tension alive, which is his strength.

                1. Input
                        \
       2. Processing -> 4. Art -> 5. Science -> 6. Morality
                        /
                        3. Output -> Feedback

Life & cybernetics. The “Arts” encode aesthetics, religion, theology, and spirituality of mankind throughout history & have the distinguishing feature of reverence. It is the irreverent sciences that make inferences strictly by testing the present against historical data. Morality seeks deliverance from the evil deeds of history, present, and future. It borrows from science by interpreting then critizing history & from the arts by reinterpreting everything afresh “as if experienced first hand.But its a third layer of abstraction, whereas art is the first level of abstraction that reharms life as experienced

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qui {
clear 
rm code.png   
global repo "https://raw.githubusercontent.com/abikesa/philosophy/main/kitabo/ensi/data/" 
if 0 {
    See 'pdfs/Work Note.png' for App
//1. data-analysis: cox-regression (rdc restricted-access)
stcox var1-58, basesurv(s0_nondonor)
matrix b=e(b)
mkmat beta1-58, matrix(b_nondonor)
keep _t s0_nondonor
export delimited s0_nondonor.csv, replace 
}
//2. processing: beta-coefficients (zero disclosure-risk)
import delimited "${repo}b_nondonor.csv", clear 
local i = 1
foreach var of varlist * {   // this loops over all variables in the dataset
    rename `var' var`i'
    local i = `i' + 1
}
mkmat var1-var58, matrix(beta)
//3. flexing: scenario-vector (demonstration-only; app for eui)
import delimited "${repo}SV_nondonor.csv", clear 
local i = 1
foreach var of varlist * {   // this loops over all variables in the dataset
    rename `var' var`i'
    local i = `i' + 1
}
mkmat var1-var58, matrix(SV)
//4. art: base-case (embodies, realizes, transcends usual stuff)
import delimited "${repo}s0_nondonor.csv", clear 
l in 1/10
g f0 = (1 - s0_nondonor)*100
//5. science: logHR-se.logHR (decodes everything, communicates to fellows)
matrix logHR=beta*SV'
matrix list logHR
//6. morality: threshold, draw-the-line (all 'bout dre for the eui)
g f1 = f0*exp(logHR[1,1])
line f0 f1 _t, ///
   sort connect(step step) ylab(0(20)100) ///
   legend(lab(1 "Base-Case") lab(2 "Scenario"))  
graph export ../figures/code.png, replace  
}

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file /Users/apollo/.stata_kernel_cache/graph1.svg saved as SVG format
file /Users/apollo/.stata_kernel_cache/graph1.pdf saved as PDF format

import delimited "${repo}beta_coefficients_58.csv", clear 
list variable

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(encoding automatically selected: ISO-8859-1)
(2 vars, 58 obs)


     +-----------------------------+
     |                    variable |
     |-----------------------------|
  1. |                 diabetes_No |
  2. |                diabetes_Yes |
  3. |                  insulin_No |
  4. |                 insulin_Yes |
  5. |                 dia_pill_No |
     |-----------------------------|
  6. |                dia_pill_Yes |
  7. |             hypertension_No |
  8. |            hypertension_Yes |
  9. |     hypertension_Don't_Know |
 10. |                 hbp_pill_No |
     |-----------------------------|
 11. |                hbp_pill_Yes |
 12. |                    smoke_No |
 13. |                   smoke_Yes |
 14. |       income_adjusted_<5000 |
 15. |   income_adjusted_5000-9999 |
     |-----------------------------|
 16. | income_adjusted_10000-14999 |
 17. | income_adjusted_15000-19999 |
 18. | income_adjusted_20000-24999 |
 19. | income_adjusted_25000-34999 |
 20. | income_adjusted_35000-44999 |
     |-----------------------------|
 21. | income_adjusted_45000-54999 |
 22. | income_adjusted_55000-64999 |
 23. | income_adjusted_65000-74999 |
 24. |      income_adjusted_>20000 |
 25. |                      <20000 |
     |-----------------------------|
 26. |          income_adjusted_14 |
 27. |          income_adjusted_15 |
 28. |           Refused to answer |
 29. |                  don't know |
 30. |               gender_Female |
     |-----------------------------|
 31. |                 gender_Male |
 32. |                  race_White |
 33. |       race_Mexican_American |
 34. |         race_Other_Hispanic |
 35. |     race_Non-Hispanic_Black |
     |-----------------------------|
 36. |                  race_Other |
 37. |                     hs_Good |
 38. |                hs_Excellent |
 39. |                hs_Very_Good |
 40. |                     hs_Fair |
     |-----------------------------|
 41. |                     hs_Poor |
 42. |                     Refused |
 43. |                           8 |
 44. |                  don't know |
 45. |            education (none) |
     |-----------------------------|
 46. |                          k8 |
 47. |            Some High_School |
 48. |      High_School_Equivalent |
 49. |                   Associate |
 50. |             College or more |
     |-----------------------------|
 51. |                     refused |
 52. |                       age_c |
 53. |                    bpxsar_c |
 54. |                    bpxdar_c |
 55. |                bmi_centered |
     |-----------------------------|
 56. |                      egfr_c |
 57. |                      uacr_c |
 58. |                        ghb  |
     +-----------------------------+