The Problem of Calibration

[But] through Galileo’s mathematization of nature, nature itself is idealised under the guidance of the new mathematics; nature itself becomes – to express it in a modern way – a mathematical manifold [Mannigfaltigkeit]

(Husserl, 1970:23)

135From ethnographic notes: Aim: the final cost of the air quality egg device should be less than or about 50$, and should have at least four sensors on it. From the interview with participant (ICI7):

‘I didn’t know much about sensors themselves. And siting around here I am finding out more about them, about the ones that are being used, and these are cheap enough ones, and also about the ones which are not cheap enough and therefore not being used, but should be, something we need to look at more.’

For Husserl, early in the twentieth century, the problem of abstraction, through the art of measurement, lay at the heart of what he saw as the crisis of European sciences. In the introduction of his contemplation on the crisis of scientific approach¹³⁶, he made a point that from the times of Galileo and even before, the achievement of ‘true’ knowledge and ‘objective being in the world’ has meant an on-going overcoming of the relativity of the subjective interpretation of the empirically intuitive world, and subsequently ‘constantly increasing approximation’, through idealising and mathematisation. However, for Galileo in his time, this approach (then only practised in the field of geometry) had its flaws when it came to the measurement of specific sense-qualities of the world, or what Husserl called the plena of the world. In abstracting this sense-quality aspect of the world, Galileo mastered indirect mathematisation and apprehended the meaning of causality and its role in the interrelationships between shape and plena, and consequently the basic principle of observer-relativity.

Five hundred years later, the developers in this community of practice, working on measurements of air quality, again find themselves negotiating their path to objectified knowledge, thus continuing the never perfect art of measuring. As we can observe in the following utterance in Example:11, the speaker (IC_4) is very casual about the validity of data produced by the first prototypes of the Air Quality Egg device, deployed during the Citizens Cyberscience Summit workshop.

Example:11

1. its getting information, whether that information is pertinent that’s ...

2. that’s going to be a long haul of this and...

3. I think that there is lot more presentations today, specifically about 4. validating this data scientifically and validating under the research

and then also validating it personally.

5. And I think that ...

6. the personal approach is gonna be much more successful,

7. I feel, right away, then ...then a research approach, I think...that 8. It…it hopefully won’t trigger kind of a...a...a...

9. cried wolf scenario, where we stand up and yell about air quality when ...

10. we don’t really have a problem but ...

11. I am hoping we are on a right track that...

136'Discovery is really a mixture of instinct and method,' wrote Husserl in The Crises of European Sciences when analysing the Galilean idea of universal physics. (Husserl,1970:40)

12. Even if our data is not valid scientifically or in that ...

13. technical ...direction,

14. I hope that we get the right information to say: there needs to be more study.

15. That air quality sensors like this really can be em...

16. go to for application of real high-end sensors, I mean...

17. The government has picked places to put sensors, but what if we can say

18. “well, there is real pockets here that you need to be careful of”, 19. or “right around the school there is this traffic intersection that you

know...

20. we have really weird readings and that we have changed our sensor ... times and still getting really weird readings”, you know – 21. further investigation!

22. And that’s a win right there...you know...

23. Whether or not... the data is great or not 24. I don’t really care.

The indexical ‘its’ in line 1 refers to the foreground framing, the progress in the development of the Air Quality Egg, discussed before this utterance. The indexical

‘that’ in the second part of the sentence (line 1) refers to the previous part of the sentence, i.e. the information received from the Air Quality Egg device. The following ‘that’ in ‘is going to be a long haul’ (line 2) refers to the discussion about whether the information received is valid, while ‘this’ refers to the upcoming sentence were the specificities of validity and the context of discussion are examined.

The prognostic framing here works within the motivation framing, pointing towards a kind of acceptance of the ongoing process of negation that is an integral part of the project itself. In lines 3-7, framing devices of exemplars and time shifts are utilised.

The speaker turns to the concreteness of current space-time and the talks that are happening during the summit, where discussions about the data validity of this and other citizen science projects are taking place. Three validation approaches belonging to the mental frame of citizen science are presented: scientific, research and personal.

Here, the presence of another mental frames surfaces as the speaker presumes that his perception of what classifies as research or personal approach is commonly agreed upon. In line 5, the speaker announces with a strong indexical ‘I think’ that the personal approach will be more successful than that of a research approach. By aligning himself with the personal approach and by expressing such certainty of its success, he points towards the presence of a primary frame that could be identified

here as a political form of assembly, identified with the global citizens movement¹³⁷ and the recursive public¹³⁸, encompassing both the open source and citizen forms of organisational frameworks.

The reasoning for his strong statement and specificities of the primary framework become more apparent in lines 12-23 when the speaker employs the depiction of possible scenarios, which does not really explain the personal approach any further, but does, however, illustrate the situation where citizen sensors could be distributed more densely and located on a much more local level, in locations that might matter personally (lines 18-20). The boundary framing is activated in line 17 in which the collective action frame of citizen science is positioned in opposition to a government and its sensor distribution. Likewise, the boundaries and distance are drawn between

‘air quality sensors like this’ (line 15), with ‘this’ pointing towards the sensors developed within this group, and ‘real high-end sensors’ (line 16) identified as ones been used by the government or industry – or in other words the other, outside the collective action frame.

The key to the understanding of data validation processes employed by this group is found in lines 12-14. In line 12, the speaker acknowledges the fact that it could be that the data, identified as ‘our data’, might not be scientifically valid. It is not, however, clear if the possessive adjective ‘our’ in front of ‘data’ refers to a particular data generated by the Air Quality Egg device, or it represents the broader, possibly DIY or open source community, approach to data validation. Nevertheless, as the statement is made in the context of the organisational framework of this particular community, I would suggest that ‘our’ here refers to the approach taken by this exact community of practice that clusters around the Pachube platform and its satellite projects.

137For more on the global citizens movement see:

http://en.wikipedia.org/wiki/Global_citizens_movement

138See Kelty (2008): “a public that is vitally concerned with the material and practical maintenance and modification of the technical, legal, practical, and conceptual means of its own existence as a public; it is a collective independent of other forms of constituted power and is capable of speaking to existing forms of power through the production of actually existing alternatives”

(Kelty, 2008:28).

From lines 12-14, we can also learn that the nature of this inadequacy, in scientific terms, is to do with a technical issue or, as we learned previously, due to the shortcomings of the sensors themselves. However, the validity of ‘our data’ is explained in terms of information. Thus the difference between the data and information is made explicit. As further elaborated in line 20, the information is created by the changing values of the sensor data, while calibration is done through the comparison of different sensor data. An interesting peculiarity pointing towards the collective vernacular can be observed in line 22, where the speaker identifies the value of acquired information via comparison of data and its local meaning with the

‘win, win’ situation. This not only suggests that the identity of the group is sustained in dialectic opposition, but also notes the importance attributed to playfulness, highlighted earlier.

Other participants also raised the issue of calibration by means of comparison. In the following Example:12, participant ICI_6, while being optimistic about the community’s involvement in the development of sensing devices, expresses her deep concern about the approaches taken regarding the calibration process.

Example:12

1. but I am concerned with ....

2. actually at the mo(ment)...the issue ...

3. the issue there is one of calibration.

4. It's no good saying it's worth, it's better... worth or better than what?!

5. And that’s where the issue comes from.

In lines 2 and 3, the speaker makes a repair of the utterance made in line 1 in which she expressed concern. She replaces her ‘concern’ with the ‘issue . . . of calibration’

that she is concerned with at this current stage of the development, marked by ‘at the mo(ment)’. The issue is elaborated in line 4 as a method of comparison. What is not clear is what data gets compared with what.

The issue related to calibration could be temporary, and might not qualify as distinctive or exclusive to this particular community of practice. However, as almost all interviewed participants commented on this issue, it could be assumed that it forms an important part of the very identity of this group: the mastering of the art of

measuring. The following Example:13 illustrates well the length and the complexity of the multiple issues involved when an attempt is made to compare two or more specific sense-qualities, or unlike things, or two sets of technically inaccurate data.

Example:13

1. I do use other peoples data and they use mine. Em...

2. as a... you know, to compare...

3. but there is a problem with that, which is that at the moment 4. there is no calibration of buildings, em..

5. other than it's a very crude building codes, 6. which are not very useful.

7. And anyway, I even don’t know what is the building code of my building is,

8. because you can’t...

9. you can’t do that yourself. That’s a ...

10. You have to pay to have that done, so...

11. I sort of don’t care because it is not precision information, so...

12. What would change that ...

13. and again you could do around Pachube, or you could do that as separate initiative...

14. would be to calibrate buildings, so that...

15. you say: ok mine is five bedroom family house in a suburbs and 16. I find comparing to somebody else

17. either I want to compare to with almost identical place or 18. I want to have some kind of rating system that I can fracture it, 19. so then I know em...how we doing relatively.

20. If we are having like an energy competition...

21. So coming back to my colleague and I, we are having this completion to see how much

22. energy we can save...

23. that was a little bit easier for us because I know, you know...

24. I go to his house, I know the house. He knows mine.

25. So when we comparing, we know how to make that comparison.

26. and I think, that’s a problem with shared data.

27. Shared data is great but ...

28. kilowatts per house, or something...

29. some measure like that is kind of not useful.

30. You need to have some kind of calibration system,

31. some kind of profiling system in order to be able to make comparisons.

32. Say and if you are talking about health, lets say.

33. Say we compare my daughter and I, right, 34. she is training for a marathon and I would die...

35. if I go ...and if I would try and do one((LG)).

36. So, you know, you can’t...

37. however, we... I might I might be trying harder than her

38. in a relative sense and should be getting brownie points for that, 39. if we are having a competition.

40. So all those kind of calibration things, calibration of measurements and also

41. comparability between unlike things you are measuring 42. that’s all yet to be done.

In Example:13, utterances are made in response to the question about the collaborative aspects opened up by shared platforms such as Pachube. The interviewer asked if participant ICI_7 had some data on this system and whether he had some knowledge of other community members with similar data sets. This lengthy response, while confirming the participant’s engagement with the other people’s data on this data-sharing platform, is effectively saying that while the theory of shared data is great, the practicality of it is currently very problematic due to calibration.

In line 1, the speaker reiterates the question in his own words, affirming his usage of other people's data for reasons of comparison (line 2). In lines 3-11 the speaker shifts the focus to a mental frame that works here as a background for an immediate issue frame, which could be a derivative of the master frame – legislative system of the build environments, its historic materiality and state or council management systems.

The task here is to make a diagnostic assessment of the context for the foreground framing, i.e. lack of ‘calibration of building’ (line 4), ‘very crude building codes’

(line 5) that ‘can’t be done by yourself’ (line 9) and ‘must be paid to be done’ (line 10), and finally no one really being responsible to resolve this (lines 10-12).

The core issue, revealed in lines 23-29 as the problem with shared data and unrelated values, is seen as a temporal issue (line 3), something we have already observed in perceptions of other participants in relation to calibration. This suggests a notion of the problem’s temporality and the belief that it will be overcome. The same confidence is expressed here even when the diagnostic framing leads toward the problem, beyond the direct powers of an individual or the group. This might be explained by a larger master frame being at work here, that of citizenship, identified by scholars previously (Berger et al., 1972; Statham and Mynott, 2002; Koenig, 2004).

Here it can be identified by the way the speaker employs prognostic framing (lines 13-19) to overcome different complex issues concerning building calibration. The solution is sought by applying methods (line 13) characteristic of ‘do it yourself’ or open source culture, identified with this community of practice or, more lately, by the crowdsourcing approaches of citizen science, such as to ‘create some initiatives’

or use existing ones (line 13), around which people could participate by describing their own houses for common purpose, for example. In such a scenario, the measurements could be done on the basis of relative value comparison (line 19).

In lines 20 to 25, the speaker employs an exemplar that refers to a story he shared earlier on in the interview. The story involved a competition between the speaker and his colleague over who would save more energy in a set period of time. While the speaker turned to the installation of an energy monitoring system, his colleague applied analogue methods such as checking the meters and writing the values down with pen and paper. The competition not only saved the speaker money but also taught him valuable lessons through doing, or praxis. The speaker admitted learning how his heater works. He also learned how to construct graphs from his data streams, and came to the realisation that the biggest saving was done not by automation but rather by remembering to switch off the lights when not needed.

In Example:13 (line 25), the speaker reveals one of the important elements necessary for any calibration based on comparison – to know the relevant elements involved in the comparison. Here, knowing is associated with that of physical experience, of being in the house (line 25 ‘I go to his house’); that would imply knowing all the variables such as how many rooms are in the house, where it is located, when it had been built, etc. (line 15). After reiterating the problem in lines 30-31, where he introduces a new term ‘profiling system’, the speaker turns to another example that illustrates the shared data and calibration problem, this time in the health sector (line 32). Here, the speaker not only appropriates a framing device to reach out to his audience and explain the meaning of ‘profiling system’, he also introduces another mental frame – data mining across many sectors and fields of life.

In this example (33-39), the speaker introduces the comparison between himself and his daughter competing to run a marathon. In lines 34-35, the speaker presents two very different states one would need to compare. The daughter, who is training, and his, that he ‘would die’, implying his age, his identity as a geek sitting down a lot, and the fact that he has never even considered running a marathon, marked with ‘if I would try and do one’ (line 35). Likewise, the utterance is expressed with a disbelieving smile and followed by humble laughter. The other variable is introduced in line 37 – he ‘might be trying harder’- followed by another in line 38 when he talks of the ‘brownie points’ he should receive for even trying, bringing to the foreground the perception of self as one that simply would never consider running, as well as the social mental frame of normality with regards to age and physical activity.

Lines 40-41 bring the issue frame into the foreground again by reiterating the key problem with shared data and calibration, that of heterogeneity ingrained in the nature of things, i.e. measurements and standards. By choosing the solution to the problem, as he did in lines 12-13, the speaker aligned himself with the previous speaker of the utterance in Example:12. Both speakers chose to take a personal approach to data tagging, conducted in a framework of responsible citizenship. This approach is celebrated by all members of this community (observed during my study), and to an extent defines the core method of calibration applied in this community of practice.

4.5. Conclusion

By looking at the linguistic markers, assemblies and vernaculars utilised by speakers in this community of practice, this part of the study aimed to identify the perceptual frameworks informing their practice, and the subsequent broader development of the Internet of Things. I began the analysis with an examination of the speakers' motives for participation, and argued that frame analysis of their speech acts provides not only clear categories for their reasoning but also gives an insight into the structure of community formation itself. For example, while much of this community's activity could be characterised in terms of a community of practice, there are visible indicators of broader social systems involving other structures, projects and

associations. The alignment with contexts such as open source movements, DIY

associations. The alignment with contexts such as open source movements, DIY