The following paper was presented and discussed at the Brock University Learneds Society CAPDU meeting in June, 1996.
But this is only the tip of the iceberg. As important as it may be to satisfy existing demand for data, our long term objective should go much beyond. We want to increase the amount of socially relevant knowledge available to Canadians in public debates; we also want to involve as many people as possible in creating such knowledge, and in transforming it so as to fit various purposes at hand.
Spreading the data culture entails not only producing more knowledge, but also making more people knowledgeable. It means involving ever larger circles of people in appropriating and understanding knowledge, in criticizing it, and even in producing it themselves. This is indispensable for reasons that have to do with both the nature of social scientific knowledge, and the challenge of a democratic modern society.
The validity of scientific knowledge, social and otherwise, rests entirely on its total openness to criticism; this in turn pre-supposes that there are enough appropriately trained producers and critics of this knowledge. The difficulty is particularly acute for a relatively small society like ours; our problems are not the least bit simpler than in larger ones, and yet our resources are much less abundant. We need to mobilize as many people as we possibly can in the task of understanding the dynamics of our society, and of helping it through its current crisis. Moreover, social science is no magic bullet, ripe with ready made solutions: it application to issues requires the involvement of all interested parties, and acting otherwise usually brings about misery and perverse effects.
Indeed, the complexities of issues facing our society will require an increasingly sophisticated citizenry and workforce. Democratic debates around questions such as the coexistence of different cultures brought about by modem communications and 2 migrations, ecological problems, the dialectics between the values of equity and freedom, new forms of concerted socio-economic action, etc., require complex information and relatively sophisticated participants. Economic prosperity will also increasingly rest on intellectual, managerial and cultural inventiveness, rather than on the sheer availability of resources and physical capital; much of that inventiveness will depend on how widespread is the ability to decipher the signals coming out of social processes themselves.
Literacy, then numeracy, and, even more recently, some computer literacy have become preconditions for a full participation in contemporary social life; similarly, a minimal capacity to interpret social data will become key to leading a productive life, and to being an informed citizen. In this sense, we cannot be too ambitious in setting the goals for the second phase of the Data Liberation Initiative: our objective should be to spread the data culture, to reach various publics with appropriate amounts of knowledge about how to interpret social data, to generalize recourse to such data in social, political and economic debates. Of course, quantified data are not, by far, the only pertinent source of social information; indeed, we are increasingly awash in a world of archives, textual, verbal, and visual, and we should also learn how to exploit such data. In this sense, the current phase is just a waystation in the generalization of social sciences education; we focus on social statistics because the DLI currently provides us with an invaluable opportunity, but we should situate our enterprise in a broader context.
Wishing for a generalization of social sciences education may seem far fetched.
A few examples demonstrate, however, that elements of social science knowledge have indeed already become part of current discourse. Opinion surveys, for instance, have become familiar to most voters, who probably even know a few things about response rates, intervals of confidence, various methods of redistribution of non-responses, etc. Many key economic concepts, like inflation rate, unemployment rate (even in their deseasonalized version), trade balance, Gross Domestic Product, income polarization and the like are used in debates and in the media, and sections of the public are even beginning to consider criticisms of them (such at the contrast between the Gross Domestic Product and a recently proposed Genuine Progress Indicator). Similarly, some amount of knowledge about socio-professional categories, immigrant groups, the changing roles of women, new family forms, and the like regularly shows up in discourse. Spreading the data culture simply means strengthening this trend, systematizing references to precise definitions and data, and increasing the capacity to criticize these definitions and measurements.
The figure identifies the different publics which this new phase of the DLI should reach - at least in principle. It is organized in sectors, within each of which social science is produced and used in different ways. At the centre are the professional producers of social science knowledge; at the periphery, the public is at once the object, the beneficiary, and a shaper of this knowledge; in between, one can identify different categories of users and creators of such knowledge, according to their needs and to their level of sophistication in understanding and performing research. The sector corresponding to private enterprises has been blanked out, not because it doesn't need and use social science knowledge (quite the contrary), but because spreading the data culture outside the public sphere implies a different logic, that of profitability; for this reason, the private sector has not been included in Phase 1 of the DLI, and it keeps obtaining its data in its own way. This being said, co-operation between the public and private sector is not only conceivable, but might turn out to be a key ingredient for some aspects of Phase 2 of the DLI.
Under each of the publics identified in this first figure, numbers indicate similar training needs with respect to the use of social data. The next figure crosstabulates these publics - regrouped as indicated by these numbers - and the various training needs; the latter correspond to a number of technical areas of expertise (from elementary to advanced), but also, and even more importantly, to the art of asking fruitful questions, and of asking them in appropriate ways. An "x" in any cell of this second figure indicates that a given public, or set of publics, should be provided with training in a particular area.
This is, obviously, much too much for Phase 2 of DLI; Phase 1 has demonstrated that we should not shy away from ambitious objectives, but also that we should aim at achieving a few successes within a reasonable time. As a consequence, these figures should be used to select our priorities. We should launch the discussion of Phase 2 with questions such as the following.
What has to be done on a short term basis, as a precondition for the whole DLI scheme to produce results? Training of research librarians in how to identify, acquire, store and make available pertinent data would seem to belong in this category, for instance.
Which publics can be regrouped for the purposes of common training activities, depending on their interests, their needs, their institutional affiliation (or lack thereof), the compatibility of their timetables, etc.?
To what extent should these activities be organized around substantive subject areas, such as health, welfare, ecology, the economy, culture, etc.?
Which of these needs should be served through existing structures and activities, and which require new organizations? How and with whom should the Phase 2 Task Force undertake discussions about priorities and division of labour?
