In some countries downloaders and uploaders are regarded as criminals: in the US you can expect litigation by record companies and artists when you share your music. In France your Internet connection wil be cut off when you’ve been downloading music and/or video’s. This shows that the Internet isn’t a safe haven for people that want to freely share information.

This is totally opposite to what the founding father of the World Wide Web Tim Berners-Lee thinks it should be. Berners-Lee is the advocate for free data. Organizations, in particular governments, should open their databases on-line, creating a level playing field for all. It also allows for members of the general public to contribute to datasets. Also, people can, if they have the expertise, to analyse these data and share the results in a numerical or visual style. This can be risky because as is possible with numerical information, visualizations can be deceiving. The proverb “there are lies, damn lies and statistics” should be “there are lies, damn lies, statistics and visualizations”.

A few initiatives are the US government, the UK government, and the Guardian. The sharing of data is, or it should be, common practice in scientific circles. In the Netherlands DANS archives scientific data and grants access (however, not for all). In academics, unfortunately, it doesn’t always pay to share your data for an important reason. Increasingly academics are told by university management to publish in ISI-ranked journals. That’s OK. But archiving data, which is important for secondary analyses and enabling others to check your work, takes a lot of time but is not rewarded by management. The time it takes to prepare the data and report for archiving could also be spent on new research articles and data collection. So, given the choice between a time consuming unrewarded archiving and writing new manuscripts, the choice will often be the latter. Unfortunately, this can only change when universities reward archiving the same as journal publications. I feel this is unlikely to happen in the foreseeable future.

Below are two videos where Tim Berners-Lee explains the idea and shows some examples.

Last week there was a lot of discussion about Ross Dawson who predicted the time of death of the newspaper in several countries. Piet Bakker devoted a blog to it as did Trouw (there are probably many more sources so let me know in the comments). It’s important to know when the newspapers get extinct, so then we know when to have cleared our agenda’s to go to the funeral or at least have found a new job.

All kidding aside, we love predictions (especially weather forecasts)  and we need them. However, it’s one of the most difficult things to do. All predictions are uncertain. However some are more uncertain than others. Having no additional data, predicting the end of the newspaper is similar to looking into a crystal ball, humming some unclear sounds while burning incense: rubbish in gets rubbish out. Then again, if one would actually have additional data things brighten up. What data for instance? Well, first of all the hard data the publishers already have, even the data we researchers have little unconditional access to. For instance,  (1) detailed subscription data (paid, non-paid, discounts) (2) financial data (costs,  revenues), (3) marketing strategies  (4) changes in population composition, (5) competitors’ actions and developments, and (6) autonomous developments such as general economic growth and level of unemployment, but also technological developments. Some other suggestions have been made in comments on Piet Bakker’s and Ross Dawson´s blogs, such as the writing off of printing presses, the culture of reading for each country. All more or less relevant to be taken into account.

The prediction made by Piet Bakker (i.e. time series analysis) are, in my opinion, equally uncertain (see my posted comment). Especially because he used very little observations (six years) to predict far into the future (40 years). So, the further away the prediction is the more uncertain the prediction is. Not only that, a possible bias in a short term estimate is most likely increased the further away the prediction is, as such biasing the final prediction. Also, I suspect there will be an excellerated decrease in subscriptions at the end, because newspaper publisher (or the banks) will see the end is near and will pull the plug before they loose even more capital.

There are alternative approaches to the moving averages approch, though. The first one is using multiple time series, where the dependent is the number of subscriptions and the others are the independent ones (some of which are listed above). Alternatively, a system dynamics model could be developed taking account of feedback loops, because every action may have positive effects on the dependent variable (in this case the number of subscriptions) but can have an effect on the causes as well in the future. These complex problems are called messy (cf. Vennix, 1996) because of the large numbers of unknowns. There is alo a more qualitative version called group model building. This used when hard data do not exist. In this approach experts from different background are set around the table and chart the problem at hand. Then different scenario’s are explored to foresee possible futures. Subsequently, the aim is to develop a business strategy that is robust for many possible futures. These approaches have been used for instance by Shell and the Dutch government. I’m not sure it’s too late for publishers to undertake such an exploration, what what I hope they’ve learned that it’s best to prepare for the worst case scenario when you can. For the publishers that would have been sometime in the eighties of the last century. Then again, like quiting smoking, it’s never too late.

On a final yet important note, and this may be depressing for the publishers, the readers of physical newspapers may be a dying breed. It’s in large part a generations issue. People who grew up with (their parents) having a newspaper at home are the ones that were more likely to have a newspaper subscription as well. Until the Internet took of in the late nineties of the last century: youngsters that grew up in the age of the Internet are less likely to subscribe. A similar thing happended with television: young people that grew up with only bublic broadcasting continued watching public broadcasting while youngsters that grew up with commercial television as well didn’t watch public broadcasting. So, it seems inevitable to see newspapers as we know them disappear. If they survive it’ll be not in this fashion. In the technical sense, no medium has ever really disappeared, it’s function merely changed. This is likely to happen to the newspaper as well. What is more important is how journalism will evolve the coming decades. But that’s an entirely different discussion.

Two chapters I co-authored were published in a book this week. The first one (Vergeer, Coenders  & Scheepers, 2009) focuses on explaining the time people spend on watching TV. The interesting point about this study that explanations are not only sought at the individual level (especially in terms of alternative ways to spend time) but also at the level of the television system (i.e. tv program diversity, number of commercial and PSB channels, the budget). The study uses multi level analysis to test the hypotheses. Here is the abstract:

This study aims to explain the variation in time spent on watching television in 15 European Union countries, using determinants defined at the individual level, and characteristics defined at the national level, such as the number of channels and nature of the television supply. The results of the multi-level analysis show that the number of channels in countries has no effect on time spent on television. Yet, the more diverse the program supply on public broadcasting channels in different countries, the less time people spend on watching television. However, this relation decreases when more commercial channels are available to watch. This suggests that EU citizens, having commercial channels as alternatives, avoid a diverse program supply in favor of commercial program supply.

The second chapter Westerik, Hollander, Verschuren & Vergeer, 2009) in the same volume, deals with community involvement and media use.

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