AndreasThinks

# -----------------------
# Routes: Admin
# -----------------------
@app.get("/admin")
def get_admin(req):
    auth = req.scope.get("auth")
    if not auth:
        return RedirectResponse("/login", status_code=303)
    

AndreasThinks

The Role of Randomized Controlled Trials in Criminal Justice: A Debate

Criminal justice reform has long been a contested space for research and policy, with scholars and practitioners searching for effective ways to drive change. One of the most debated tools in this context is the randomized controlled trial (RCT). Traditionally considered the "gold standard" for evaluating interventions, RCTs have recently come under scrutiny for their effectiveness in the criminal justice field.

Two key articles address this issue: Cause, Effect, and the Structure of the Social World by Megan Stevenson, and Then a Miracle Occurs: Cause, Effect, and the Heterogeneity of Criminal Justice Research by Brandon del Pozo and colleagues. Stevenson's piece is a critique of the use of RCTs in criminal justice, while del Pozo's article serves as a counterargument, challenging the validity of Stevenson’s conclusions. This post delves into both perspectives, exploring their arguments, methodologies, and conclusions, with the a

AndreasThinks

Model	AGIEval	GPT4All	TruthfulQA	Bigbench
mistral-7b-english-welsh-translate	35.44	Error: File does not exist	54.51	Error: File does not exist

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	24.41	±	2.70
		acc_norm	20.87	±	2.55
agieval_logiqa_en	0	acc	33.95	±	1.86

AndreasThinks

Model	AGIEval	GPT4All	TruthfulQA	Bigbench
mistral-7b-english-welsh-translate	35.31	Error: File does not exist	54.5	38.4

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	24.02	±	2.69
		acc_norm	20.47	±	2.54
agieval_logiqa_en	0	acc	33.95	±	1.86

AndreasThinks

AndreasThinks

AndreasThinks

from fuzzywuzzy import fuzz

drug_dict = pd.read_csv("drugs.csv",  encoding= "ISO-8859-1")
drug_dict = drug_dict.rename(columns={"Substances: Category & Name": "substance", "Examples of Commercial & Street Names": "names"})

#this function will return the highest value from the list and it's ratio of "sameness"
def compare_to_list(description, list_to_compare):
    accuracy_dict = {}
    for comparator in list_to_compare:
        for word in description.split():

AndreasThinks

import pandas as pd
import statsmodels.api as sm


df = pd.read_csv('loansData_clean.csv')

overTwelve = []
intercept = []
for item in df['Interest.Rate']:
    overTwelve.append(item < 12)

AndreasThinks

import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import numpy as np



loansData = pd.read_csv('loansData.csv')

AndreasThinks

import pandas as pd
import matplotlib.pyplot as plt

loansData = pd.read_csv('loansData.csv')


month = lambda x: x[:-6]
percent = lambda x: float(x[:-1])
fico = lambda x: x[:-4]
amount = lambda x: float(x)