Week 10: Meet or miss

This week I’ve tracked how punctual I am in my working and personal context. I was capturing time-related data for 5 days, 17 hours a day. Here’s what I have found out:
 
1)      My patterns of behavior are similar in different contexts (I joined 67% of events at work in advance or on time.  68% is the number for private matters)
2)      I stick to my plans and schedules better at the beginning of the week. It gets more challenging to keep on track by Friday.
3)      Whether I joined a meeting in advance or was a few minutes late mostly depends on my role in it. If I’m the presenter, I always start the event a bit in advance. If I’m one of 30 participants in the FYI mode, being a bit late is absolutely ok, nobody starts on time here.
 
My third point is about context that remains unknown if you look at my visualization. Is being late a norm or misconduct in my cultural and working settings? What does my contract say or what unwritten rules work in my department? These data are obviously incomplete and decontextualized, and as other kinds of statistics, they reduce the complexities of real life. However, as Grundmann and Stehr (2012) note, ‘the decline or loss of the context-specificity of a knowledge claim is widely seen as adding to the validity, if not the truthfulness, of the claim (from Ozga, p.71).
Potentially, in a neoliberal, pressing-for-efficiency society, such indicators can be part of an algorithm that measures teachers’ or school management performance and discipline. In the online world, tracking working hours and punctuality is another simple measurement that can shape policies and impact ratings.
This tracking exercise made me recollect some unpleasant stories connected with firing teachers. The thing is that if you have to fire an instructor for poor teaching here, it’s next to impossible to do it for a real reason, because ‘low-quality teaching’ is badly defined, has a plethora of interpretations and sounds very subjective. Hence, it is not in any way described in the contract. So in search of evidence for misbehavior, what we did back in the pre-Covid times, we simply requested the clock in/clock out data from the security service. Late arrivals and early leaves could be easily detected and found fault with, since they are regulated by internal policies. As ridiculous as it may seem, but a manager can rely on this kind of ‘objective’ data to make life-changing decisions. In fact, in this case, punctuality/working hours are prioritized over quality teaching, just because they are easy to measure and define.    

2 thoughts on “Week 10: Meet or miss

  1. “… punctuality/working hours are prioritized over quality teaching, just because they are easy to measure and define”. This seems a crucial line in your reflection. I think it reflects what Jenny Ozga writes about “thin descriptions” through data, and how easily accessible data–rather than complex information–become important sources of policy and governing. So we end up with thin quantitative descriptions rather than thick, contextualized, qualitative explanations as a driver for policy decisions. You can understand why–simple numbers make the world more easily legible, and more efficient to comprehend–but as you’ve pointed out, this can lead to hugely consequential decisions. In the book Weapons of Math Destruction, Cathy O’Neill recounts some of the ways data lead to action against teachers, e.g. test grades used as proxy data on teacher performance. As she argues, data about “bad test grades” don’t really tell you anything about teacher performance, but about the underlying structural inequalities that lead some students to underperform in terms of average grades. Only a thick, contextualized analysis could grasp that. I look forward to your final dataviz and reflection next time.

    • Thank you for your feedback, Ben! Indeed, O’Neill’s examples are very relevant to this context and thought-provoking too.

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