Police departments in the US derived from English law, which had a system of unpaid enforcement via gentry. Prior to that, reeves were individuals charged with keeping the peace. Prior to that it was expected everyone contributed via frankpledge.
This takes us back to around the year 1000.
There's not much evidence of law enforcement prior to that in the direct historical lineage of US policing.
In other cultures we see military (gendarmes or "men at arms") and slaves being deployed to enforce laws. (Common in Rome and Byzantine culture.)
In other cultures a leader would create justifications and appoint a single person to ensure the jurisdiction was peaceful. That single individual was paid, and they likely hired guards when they wanted, but I generally wouldn't consider a guard or posse to be law enforcement in the way we understand that term today. Those roles were closer to bounty hunters. This is how the Egyptian pharaohs ran things thousands of years ago.
Hammurabi, btw, isn't like the inventor of laws. He just very famously decided to punish violence with violence. Sumerian law existed prior. But the existence of laws says nothing about their enforcement. If you've got good resources on Sumerian or Mesopotamian enforcement, I'd be interested, but it's pretty far off topic of the US law enforcement lineage.
Plenty of communes have existed at varying scales historically.
Plus, something like Mondragon exists with a large scale. It's less commune and more workers coop at the billion dollar scale, but it's nevertheless a useful reminder that we can scale up non traditional models.
This is sort of the fundamental building block of many socialist and anarchist movements.
The concept of "mutual aid" as put forward by folks like Kropotkin is basically we should build communities that help one another, offering our communities excess when we have our, and gracefully accepting aid when we need it.
You should consider checking your humors or mediate or something, because wanting to do violence because of some Internet text is not healthy human behavior.
People were paid to protect someone's stuff, those people were called guards, but they weren't enforcing law. Some people were given ownership of a place and the people in that place, they were called lords or knights, but they weren't enforcing laws, they were enforcing their will.
Some communities had expectations and rules, and the community was self policing - if you did theft, the mob would come punish you. They weren't enforcing laws and certainly weren't paid. Their justice was uneven and often brutal.
Sometimes soldiers would be pressed into enforcing laws, but they were being paid to be soldiers.
Eventually we got to some folks who were paid to enforce laws. Some of the earliest were "Shire Reeves" (from which we derive the word sheriff). A shire reeve was a single man who was charged with keeping order in a shire. That's not exactly enforcing laws, but it's close enough in spirit. A reeve would hire temporary folks in a posse if needed to achieve a temporary goal, but did not have a police department.
Temporary posses were not uniformed, and were typically paid for short term labor - arrest this guy or get that property back. A reeve may have guards for his safety or to intimidate local folks, but those guards weren't really law enforcement.
Around the fourteenth century, governments started relying on formal roles known as justices of peace, or conservators of peace, who were explicitly charged with binding people to laws and enforcing laws. They were relatively large in number and the practice continued through to the industrial revolution. Except JPs weren't paid, they were typically gentry who enjoyed the social status it granted them.
The history of US police is really fascinating, and not something most people really every dive into. They believe it was always this way, but it very much wasn't. Police as a concept like we know them is very, very modern.
> Before describing the details of the framework, the high-level intuition is that we are reweighting the Nexar data sample, which is sampled from a non-representative set of locations, so that it matches the locations where different demographic groups actually live. For example, to calculate the police deployment levels that Asian residents of New York City experience, we reweight the original data sample to upsample neighborhoods with larger Asian populations.
> Overall, our estimation procedure compensates for two types of potential bias. Equation 2 compensates for a data bias, reweighting the Nexar dataset (which is sampled from a set of locations which does not necessarily match the population distribution; Figure 1) to match the population distribution of demographic subgroups. This is conceptually similar to inverse propensity weighting procedures [4] which are used to compensate for non-representative data in other settings. Equation 3 compensates for imperfect model performance, and allows us to check that model performance is unbiased (i.e., calibrated) across demographic subgroups.
Section 4.1 goes into the mathematical functions they use to address the data set.
Section 3.2 describes the data set and how it is geographically distributed.
> Data was provided to us by Nexar in two phases. Phase 1 consists of 3,987,835 images sampled prior to September 1 2020, and is extremely geographically and temporally skewed. Geographically, it is concentrated within the boroughs of Manhattan and Brooklyn, and does not contain data from the boroughs of Staten Island, Queens, and the Bronx at all; temporally, it overrepresents data from Thursday nights. Phase 2, which constitutes the majority of the dataset, consists of 20,816,019 images sampled after October 4 2020, and is much more geographically and temporally representative: it is sampled at all times of the day, on all days of the week, and also covers the entire geographic area of New York City.
> Because Phase 2 is much more representative than Phase 1, we conduct our primary analysis of disparities using only data from Phase 2. We additionally conduct numerous validations and bias corrections, described in §4.1, to compensate for non-representative sampling in the dataset. Geographic and temporal coverage during the Phase 2 period is very good. Specifically, 100% of hours during the Phase 2 period are covered; 99.6% of Census Block Groups (CBGs)3 have at least one image, with a mean of 168.2 images per CBG; 88% of roads contained within the borders of New York City are covered by at least one image, using data from OSMNX [6]. Figure 1 summarizes geographic data availability; Figure S1 summarizes temporal data availability.
I'm saying the opposite. Originally, enforcement was communal - it was a periodic expectation, something you did but didn't get paid for.
Then the wealthy started paying people to take the unpleasant shift, but even then the pay wasn't to enforce the law, it was pay to take over for some rich guy who didn't want to be up all night.
People whose career is law enforcement is newer and the compensation and expectations are different.
So, it's research into a topic you don't think should be researched?
Do you have any problems with the methodology or conclusions? Like, science can still be science even if it's pursued in the name of social justice. If they are cooking the books, then that's one thing, but if they are making a hypothesis, gathering data, and evaluating that hypothesis fairly using real data, then it's just science.
As a former engineer and now manager, it's not easier. It's different.
Like, yes I write much less code, but I also have to show much more empathy and be able to communicate with different people differently. Helping every engineer on the team advance their careers is tough, because everyone heard feedback differently.
Combine that with the one on ones where someone tells you about how shitty their life has been recently and how that's impacting their work. It's tough to go from roadmap planning to someone crying at you over a medical diagnosis to jumping directly to status updates to telling your boss no. It's an emotional whiplash not present in the engineer world.
Misled implies a degree of intent, the wording is ambiguous but I don't assume ill intent by the author, especially when they later spend a paragraph explaining why they did not slice by covariates.
They talk about that in the paper, noting not police departments but also prisons (eg, Rikers). But the data is collected on a per census block level, so even though there exists some outlier blocks, the overall trends are still quite visible.
Interestingly, you are correct, but you omitted the rest of the paragraph where they articulate why, emphasizing that across booth residential and commercial neighborhoods there is a striking difference in policing.
> We note that we report racial and socioeconomic disparities without attempting to control for other covariates for two reasons. First, if New York City residents of different races face different levels of police deployments, that disparate impact is itself important; it can also bias algorithms trained on downstream policing data irrespective of the true causal mechanism. Second, controlling for other factors in policing data can be difficult to interpret, introducing concerns about omitted variable bias and model misspecification which make it difficult to identify which factor is truly the “cause” of higher police deployments. For the sake of transparency and simplicity, therefore, we report results by stratifying each variable separately, noting that these disparities are themselves important but that multiple causal mechanisms may underlie them.
Keep to a standard schedule.