Thank a Cop
A website for finding UK police forces praise or compliments pages
Thank a Cop
A website for finding UK police forces praise or compliments pages
| 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) |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import statsmodels.api as sm | |
| import numpy as np | |
| loansData = pd.read_csv('loansData.csv') | |
| 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) |
| 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(): |
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|
| mistral-7b-english-welsh-translate | 35.31 | Error: File does not exist | 54.5 | 38.4 |
| 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 |
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|
| mistral-7b-english-welsh-translate | 35.44 | Error: File does not exist | 54.51 | Error: File does not exist |
| 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 |
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