why​ is writing a literature review such hard work? part two

via why​ is writing a literature review such hard work? part two

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please – not a heroic impact narrative

patter

Recently I’ve seen and read a lot of hero/heroine narratives. But no more than is usual in journal articles I’m sent to review and edit. They now seem to be popping up in research impact plans and claims about impact.

You know these heroic narratives – they are everywhere from nursery rhymes to popular films. It’s the knight on a white charger who slays the dragon, the cowboy who rids the town of lazy barflies, the cop who cleans up the burb and sends all those good-for-nuttin drug dealers and pimps to the big house.

There is a research version of this kind of narrative. You know them too I’m sure. The researcher/lecturer/professional rides into town – usually this is an impoverished neighbouhood/really dumb class/group of people/ hopeless policy agenda. Through the process of intervention/teaching/participatory or action research/evaluation the impoverished neighbouhood/really dumb class/group of people floundering around/hopeless policy agenda becomes…

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The Photographer of highly impacted landscapes by human intervention


<p><a href=”https://vimeo.com/261502643″>Tom Hegen: The Salt Series</a> from <a href=”https://vimeo.com/1854media”>1854 Media</a> on <a href=”https://vimeo.com”>Vimeo</a&gt;.</p>

Overfitting in Statistics

Figure 1.  The green line represents an overfitted model and the black line represents a regularized model. While the green line best follows the training data, it is too dependent on that data and it is likely to have a higher error rate on new unseen data, compared to the black line.

Figure 2.  Noisy (roughly linear) data is fitted to a linear function and a polynomial function. Although the polynomial function is a perfect fit, the linear function can be expected to generalize better: if the two functions were used to extrapolate beyond the fit data, the linear function would make better predictions.

In statistics, overfitting is “the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably”.[1] An overfitted model is a statistical model that contains more parameters than can be justified by the data.[2] The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e. the noise) as if that variation represented underlying model structure.[3]:45

Underfitting occurs when a statistical model cannot adequately capture the underlying structure of the data. An underfitted model is a model where some parameters or terms that would appear in a correctly specified model are missing.[2] Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model will tend to have poor predictive performance.

Overfitting and underfitting can occur in machine learning, in particular. In machine learning, the phenomena are sometimes called “overtraining” and “undertraining”.

The possibility of overfitting exists because the criterion used for selecting the model is not the same as the criterion used to judge the suitability of a model. For example, a model might be selected by maximizing its performance on some set of training data, and yet its suitability might be determined by its ability to perform well on unseen data; then overfitting occurs when a model begins to “memorize” training data rather than “learning” to generalize from a trend.

As an extreme example, if the number of parameters is the same as or greater than the number of observations, then a model can perfectly predict the training data simply by memorizing the data in its entirety. (For an illustration, see Figure 2.) Such a model, though, will typically fail severely when making predictions.

The potential for overfitting depends not only on the number of parameters and data but also the conformability of the model structure with the data shape, and the magnitude of model error compared to the expected level of noise or error in the data.[citation needed] Even when the fitted model does not have an excessive number of parameters, it is to be expected that the fitted relationship will appear to perform less well on a new data set than on the data set used for fitting (a phenomenon sometimes known as shrinkage).[2] In particular, the value of the coefficient of determination will shrink relative to the original data.

To lessen the chance of, or amount of, overfitting, several techniques are available (e.g. model comparisoncross-validationregularizationearly stoppingpruningBayesian priors, or dropout). The basis of some techniques is either (1) to explicitly penalize overly complex models or (2) to test the model’s ability to generalize by evaluating its performance on a set of data not used for training, which is assumed to approximate the typical unseen data that a model will encounter.

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“Energy and Civilization” By Vaclav Smil

Energy is the only universal currency; it is necessary for getting anything done. The conversion of energy on Earth ranges from terra-forming forces of plate tectonics to cumulative erosive effects of raindrops. Life on Earth depends on the photosynthetic conversion of solar energy into plant biomass. Humans have come to rely on many more energy flows—ranging from fossil fuels to photovoltaic generation of electricity—for their civilized existence. In this monumental history, Vaclav Smil provides a comprehensive account of how energy has shaped society, from pre-agricultural foraging societies through today’s fossil fuel–driven civilization.

Humans are the only species that can systematically harness energies outside their bodies, using the power of their intellect and an enormous variety of artifacts—from the simplest tools to internal combustion engines and nuclear reactors. The epochal transition to fossil fuels affected everything: agriculture, industry, transportation, weapons, communication, economics, urbanization, quality of life, politics, and the environment. Smil describes humanity’s energy eras in panoramic and interdisciplinary fashion, offering readers a magisterial overview. This book is an extensively updated and expanded version of Smil’s Energy in World History (1994). Smil has incorporated an enormous amount of new material, reflecting the dramatic developments in energy studies over the last two decades and his own research over that time.

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