Training employment outcomes are usually quantified by considering the number of graduates from a particular stream of training who successfully secure a position within that field within a suitable time period. This is a good method for measuring these outcomes in some fields of training. It’s perfectly logical to do this when students are training towards a trade – after all, it is only logical that most people who train in a trade will, for however long, secure employment in that trade.
This reflects the fact that they have trained in a field with directly applicable skills, as opposed to transferable skills, which another graduate might receive in a different field of training.
Transferable skills such as those used in statistical analysis could easily be carried over from a science background, such as marine biology or quantitative genetics, and cross-applied to a purpose such as writing policy for the government, or even analysing the success of training outcomes. While there are several rather noticeable differences between people and fish, the basic mathematical models for looking at population statistics and migration are transferable.
Thus, the underlying mathematical and statistical skills involved are directly transferable and can be used with little or no modification between one statistical field and another. While the training in question is pointedly focussed on studying a particular field (e.g., marine biology), and while there is a great deal of specialist knowledge which does not transfer, the underlying analysis skills which this specialist knowledge relies upon are transferable to a greater or lesser degree.
A graduate of a more ‘ephemeral’ subject such as philosophy or fine arts, on the other hand, typically does not have a large field of directly applicable employment opportunities; there are often few opportunities for formal employment outside of their respective academic fields. Moreover, outside the academic fields in question, there is little in the way of formal apprenticeship. In fact, these fields tend to be rather competitive, solitary and lean, a situation which does little to foster mentoring in an employment sense.
The arts in particular have a rather poor reputation for creating employment opportunities for graduates. This is not so much a reflection of the employability of the graduates in question as it is a reflection on the paradigm by which these things are measured. In fact, the arts have very similar long-term employment outcomes as other fields of study; it’s just harder to predict a career path in a linear fashion beforehand. It can be hard, even, to chart a course retroactively in some cases.
Thus, direct skills-based employability is really a poor reflection of the prospects of art graduates, as there is little in the way of a direct and sponsored career pathway for these graduates. Similarly, it is hard to argue for directly transferable skills in many of these ephemeral fields because while individuals will have a wide range of transferable skills, there is less of the consistency in methodology that one would find in the sciences, and thus transferable skills typically need to be assessed on a case-by-case basis.
Indeed, if one looks at the graduates of strictly academic, philosophical and artistic fields using either the models of direct application of training or of uniformly transferable skills, then their employment prospects would seem to be disastrous.
The fact remains, however, that graduates of these fields of study tend to have high long-term employment rates – a situation less due to the practical or transferable skills which their training has implicitly given them, and more from their developing an ability to ask and reframe questions.
It is not so much that these graduates are taught a set of skills with which to approach problems – rather, they have been taught a way of looking at, and interrogating, problems to which there is no obvious empirical solution. That said, this still raises the issue of relatability or relevance. This left-field way of looking at problems can be extremely counterintuitive to many people.
The relatability of the skills and methods with which people work has a major impact on how people view the practicality of the practitioners and, in turn, this influences people’s perceptions of the training that people undergo. To elevate or depreciate any of the skills or fields I have covered thus far is short-sighted. We will, for instance, always need people with practical skills; the people who can fix things which are broken, who can build houses, and who can ensure that machinery behaves as expected. These are essential skills, and most sensible people can respect them for what they are – that is to say that the relatability of practical skills is extremely high. We would abandon these skill sets at our peril and, thankfully, most sensible people know this.
The relatability of mathematics, statistics, modelling, quantifying, budgeting and accounting is less evident to many people, and thus fewer people understand the value of these things. However, it is possible to break down and explain the necessity of these methods of approaching problems. They are, after all, ‘rules-based.’ Thus, even if these skills are less relatable, they are quantifiable and predictable, and their outcomes follow suit.
The purely theoretical (including the theoretical sciences), the ephemeral, and the intellectual pursuits, on the other hand, often score very poorly in terms of relatability at a community level; they often rely heavily on jargon, and on certain pre-supposed injunctions which are typically topic- and field-specific. Even worse, they often rely heavily on metaphor and analogy to be understood at all. In short, they lack relatability at a community level, and often the participants in these fields have no especial desire to make their research relatable, nor do they desire to educate people outside of their own field. The knowledge they deal with is too specific for it to be disseminated broadly without diluting it.
It is little wonder, then, that the general public show strong support for people with practical skills, and a grudging admiration for those people who have trained in the hard sciences, accounting, law, and other quantifiable, rulebased fields.
It is equally little wonder that many people are simply mystified as to what is the point of the theoretical sciences, the humanities, and the arts. After all, much of what people dabble in in these fields is speculative, arcane and often seemingly purely a matter of opinion. Further, because these fields cleave strongly to their own methodologies, they often have little in the way of any sort of formalised structure. In short, at advanced levels at least, students often create their own briefs for study rather than relying on canons that already exist. This can seem as though they are just making it up as they go – however, this kind of freedom is actually the value of these subjects.
There are basically three areas of study which we use to make people work-ready. I like to call these reckoning, wrighting and reasoning, and each of these areas is greatly diminished in the absence of the others.
Reckoning refers to the deductive, rule-based methodologies; it is exemplified by the field of statistics. There is a methodology that overarches any project in statistics. What I mean by this is that the actual things being averaged, counted, quantified or mapped are not treated differently from any other item which is being counted, etc. In these processes, the methodology is the thing that is foregrounded. That is to say that these skills and methods can be used to do a range of things, and the supposed ‘subject’ of the study becomes abstract. The specialisation here is in the handling of the data rather than the material to which the data alludes.
Wrighting on the other hand is often not abstract at all. It’s making ... It is the hands-on, day-to-day process of making things, and making them work. This is not to say that there are no abstract or intellectual elements in these ‘making’ processes; engineering, electronics and computer programming, for instance, entail symbolic processes, and calculations that can be very complex indeed, but they ultimately work towards the creation of a thing.
Reasoning makes up the third leg of this admittedly unequal tripod. It is arguably the least necessary on a day-today basis, but in the long term it is critical and it is where much of our long-term progress is made. When a new, improved, or just terribly clever idea comes to light, it is almost always either an incremental improvement on previous work or a recombination involving more than one existing field. Without research, and documentation, and even creative leaps, much of this possibility is lost.
Of course, these things are never simple; many of the practical wrighting fields require a specialist knowledge and skills which are obscure or even arcane to people from outside the indicated field. Conversely, theoretical research is utterly dependent on accurate and repeatable research methodologies such as database manipulation. Reckoning is also dependent on intuition. You ultimately need a place to start from when quantifying the answer to a statistical problem – the answer may be affected by influences which are at first counterintuitive.
This crossover is something that is not really allowed for in the statistical modelling of employment outcomes that I referred to earlier. In this era where people are working for a larger number of employers, and in a greater range of fields than the previous generation (who in turn had more jobs than their parents) – and in anticipation that this trend will increase rather than decrease over time as more work becomes mechanised and part-time – it is fair to say that students graduating today, no matter what their field, are far more likely than their parent’s generation to retrain over the coming decades.
This tendency towards multiple employers and increasing cross-training is not adequately reflected in the simple models of employability in relation to training. After all, people typically want simple answers to questions such as “Will I get a job if I train in xyz?” An answer with a series of ifs and maybes isn’t exactly what people want to hear in these circumstances, but that is where we are heading in terms of employment, if not currently in education. So, essentially, the concept of training for one job is gradually becoming outmoded by the reality of a volatile employment market. In one way, the ideal is a more balanced education system where people are encouraged to work in a more interdisciplinary manner, picking up skills in quantitative, qualitative and cognitive areas, along with ongoing education throughout their lives. However, in the absence of this model, a more collaborative approach to education may be a simpler option.
The contemporary practice in most fields of education is having a group of people all complete a discrete task simultaneously, in competition, and without assistance. This way, we are able to judge each individual’s skill at each aspect of the task and to tell them where they meet an expected outcome or where they need to improve. This is a poor reflection of how people are expected to behave when in employment.
I am not calling for the abandonment of the skills and methods that are taught in different fields. Most fields of education have existed for a long time and have long since become adept at training students to meet the practical rigours of their fields. People who make things need to learn how to do these things, and people who create and apply rules also need to be trained – each field has its own methodologies for a reason.
It is very rare for a workplace to specialise in one of these things; most employers create teams of people who work on discrete tasks in order to participate in completing meta-tasks. After all, obscure physics makes more powerful hammers and arcane chemistry makes stickier glue. In the twenty-first century, neither of these examples are things typically discovered by an individual; results such as these tend to be the work of teams of people who display skills ranging from practical manufacturing skills through to marketing and sales.
When a business runs with a collaborative team-based model, it can boost productivity as well as improving the skill base of its employees whilst still respecting the individual skills of its workers. In this manner, a culture of collaborating and working out problems as a group can be fostered, rather than creating a more traditional hierarchical, top-down structure.
It is perhaps not terribly contemporary in this age of extreme individualism to be calling for an educational standard which promotes practical teamwork – by which I mean a type of teamwork where a project is broken down into discrete tasks (which is a task in itself) and these tasks are allocated to individuals according to talent and skills. However, this approach, when handled mindfully, can lead to an improvement in everybody’s skills, and thus their employability, without diminishing the individual talents of the members of the group.
E M Davidson graduated with an MFA from Massey University in 2012. Since then, she has divided her time between writing, photographing and working as a technician. In her spare time she can be found (or not found) wandering about New Zealand and Australia gathering photographs and stories.