This week another groups article, Code Switch by Janet Abbat brings out some attempts at what making coding more accessible to many different types of communities might look like. I wanted to look more into TransTech and do some investigating on their website.  It is clear that they engage heavily with teaching computer coding to trans people and offer many job training packages. What I wanted to see, and I guess what Abat is claiming they do, is get trans identifying people jobs at technology firms. Whats missing from this respective website is how successful they are at this endeavor and what companies they have been able to reach. I also wanted more testimonials and clips of real people, perhaps some video updates of what past TransTech associates are doing.

I think, in all seriousness, that it will probably take more time than one conceptually wants to imagine to make these goals more mainstream and attainable in the computer science sector. I am also cautioned to believe what “diversity” does exist in the CS field. I have recently been in conversation with a friend currently taking a sociology course about Asian Americans and the significance in which even though they are largely represented in the computer cluster they are seldom ever anything other than entry level programmers or developers thus falling victim to these “model minority” tropes elicited by the technology cluster.

During our group discussions on Monday, TJ raised points about the actual effectiveness of making initiatives teaching coding, in that even if it is promoted, there is still likely to be a disparity in outcomes by race. There are schools in black and Latinx communities that don’t even having working computers; these groups already begin with a disadvantage in access to technology in schools and digital literacy, and even if funding is given for coding education, that doesn’t mean that it will bridge the gap. This made me realize that, while Obama’s initiative seeks to create equal opportunity, much more has to be done for it to be reality.

There was also a point raised about differences between “coding” and “programming;” more specifically, the relative benefits of teaching how to work with a certain language and developing that way verses teaching students the underlying principles with problem solving. The latter would make it such that students would be able to adapt to changes even further, while the former might prepare for a job that might one day evolve past their capabilities or become obsolete.

Connecting these two threads to American history, specifically the space race of the 1960’s when the US government poured large sums of money into higher education to promote aerospace engineering, it seems the US has a history of creating large scale programs designed to promote the economy of the future while failing to address underlying systemic problems. There are students who already have the skills, and more important, resources necessary to become effective coders and programmers; these are the people who benefit from these programs. The students who they are probably intended for, meanwhile, have began with a late start. For computer science initiatives such as Obama’s to benefit the one’s who need opportunity the most, then it seems that more attention needs to be paid to underlying issues that have prevented development in the first place.

Through both drawing connections to and highlighting contrasts between intentions of the early computing era of the 1960s and the recent movement at the beginning of the 2010s, Abbate highlights the various methods of programming as what has for a long time and by many been declared a source of empowerment. In doing so, Abbate delves deeper into the true intentions of these movements.

In the 1960’s technological advancements in computing technology produced a manifold of social, economic, and educational impacts. Some touted a Utopian vision of computer programming jobs as a form of liberation for historically disadvantaged identity groups. Abbate problematizes that story with the voice of Walter DeLegall of Columbia University’s CS department who detailed the need for culturally relevant pedagogy.

At the time there was a relatively small labor market of computer programmers and the increasing demand for highly skilled labor. Over the course of that decade two learning languages, PLATO and LOGO were popularized as tools for teaching mathematical concepts and algorithmic thinking to children. Coding trade schools, with varying efficacy, were also developed and marketed to meet the growing demand for skilled labor. However, many social issues that coding claimed to solve were reproduced in the tech labor market.

After the 1960’s, programming was introduced everywhere. From elementary school to politics, movements like Code.org taught the young and old to program so they can fill the gaps in the digital economy. Since minorities had no insider knowledge of the job market, they were left behind. To fix this discrimination, identity-based institutions like #YesWeCode changed the goal of programming from one that fills jobs to one that fosters social change. Using the coding skills taught at these institutions, minorities could solve problems that affected their communities instead of building applications of no relevance to them.

Through outlining the true background of employing the empowerment argument as a push towards coding, beginning decades before the current coding movement, Abbate calls attention to the fact that this argument isn’t new. She notes that much like in many fields, “empowerment”  has acted as a front for other objectives or end games.

Towards the end of the document, Abbate infers that acknowledgment of the deep-rooted discrimination and divide in the computing world (among everywhere else) is needed and must be challenged at a broader level. Abbate herself sets this example by intentionally unifying and using the terms “programming” and “coding” interchangeably in an attempt to bridge the established divide between the words.

Sean: Body I and II

Luis: Body III and IV

Kate: Intro/conclusion 

The 16th chapter of O’Reagan really focuses on very specific history of programming,notably languages.  The reading starts with machine languages, to assembly languages, to early high-level procedural languages such as FORTRAN and COBOL, to later high-level languages such as Pascal and C and to object-oriented languages such as C++ and Java. Programs themselves are classified in terms of generations and grow increasingly more advanced as each generation multiplies. There are 3 distinct types of programming languages; imperative, functional and logical. Imperative languages requires a specific instruction and action to be given to the computer and results in a change of state. The entry level class here, CSC 151 is an imperative problem solving pedagogy where the new programmer simply masters the skill of giving instruction to the computer. Functional programming languages, like Miranda, have no global state, and programs consist of mathematical functions that have no side effects.Logical programming languages, like Prolog, define what is to be computed, rather than how the computation is to take place.

The latter half of the reading delineated various other types of more advanced programming languages. There are Object-Oriented Languages, such as Python, C++ and Java. These languages operate using objects, which can be a set of attributes such as a list in Python. This particular type of computing was very revolutionary at its inception. Functional Programming Languages mainly analyze and evaluate mathematical formulas. Some examples of these languages include Miranda and Lambda Calculus. Finally, Logic Programming Languages use mathematical logic to denote the  problem itself rather than describe the process by which the problem will be solved. The objective is the important aspect in these languages, not how one arrives at the objective. Prolog is an example of a logic programming language. The chapter concludes with an introduction to the idea of Syntax and Semantics as it pertains to computers. Syntax is the order of various components of a computing language whereas semantics is the desired meaning or intended operation of the code.

TJ–> summarized first half and posted

Gray–>summarized second half