Organic food: what we know (and do not know) about consumers

This paper reports on the latest contributions to over 20 years of research on organic food consumers. There is a general consensus in the literature on the reasons why people buy organic food. However, there is also a gap between consumers’ generally positive attitude toward organic food and their relatively low level of actual purchases. Product differentiation based on intangible features, such as credence attributes such as organic, in fast-moving consumer goods categories is enjoying rapid growth. However, there are many difficulties with research in this area, including the errors inherent in research that relies on consumer self-reporting methodologies. Further, in relation to organic food, there is a divergence between consumers’ perception of its superior health features and scientific evidence. Fresh fruits and vegetables are of vital importance to the organic sector as they are the entry point for many customers and account for one-third of sales. Further, although there is a small proportion of dedicated organic food buyers, most sales come from the majority of buyers who switch between conventional and organic food purchases. This paper identifies the practical implications for generic organic food marketing campaigns, as well as for increasing sales of specific products. It concludes with suggested priorities for further research

Estimating the power spectrum covariance matrix with fewer mock samples

The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues. The next generation of galaxy surveys will require thousands of large volume mocks to determine the covariance matrices to desired accuracy. The errors in the inverse covariance matrix are larger and scale with the number of P(k) bins, making the problem even more acute. We develop a method of estimating covariance matrices using a theoretically justified, few-parameter model, calibrated with mock catalogues. Using a set of 600 BOSS DR11 mock catalogues, we show that a seven parameter model is sufficient to fit the covariance matrix of BOSS DR11 P(k) measurements. The covariance computed with this method is better than the sample covariance at any number of mocks and only ~100 mocks are required for it to fully converge and the inverse covariance matrix converges at the same rate. This method should work equally well for the next generation of galaxy surveys, although a demand for higher accuracy may require adding extra parameters to the fitting function.Comment: 7 pages, 7 figure

A Detection of the Baryon Acoustic Oscillation Features in the SDSS BOSS DR12 Galaxy Bispectrum

We present the first high significance detection ($4.1\sigma$) of the Baryon Acoustic Oscillations (BAO) feature in the galaxy bispectrum of the twelfth data release (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample ($0.43 \leq z \leq 0.7$). We measured the scale dilation parameter, $\alpha$, using the power spectrum, bispectrum, and both simultaneously for DR12, plus 2048 MultiDark-PATCHY mocks in the North and South Galactic Caps (NGC and SGC, respectively), and the volume weighted averages of those two samples (N+SGC). The fitting to the mocks validated our analysis pipeline, yielding values consistent with the mock cosmology. By fitting to the power spectrum and bispectrum separately, we tested the robustness of our results, finding consistent values from the NGC, SGC and N+SGC in all cases. We found $D_{\mathrm{V}} = 2032 \pm 24 (\mathrm{stat.}) \pm 15 (\mathrm{sys.})$ Mpc, $D_{\mathrm{V}} = 2038 \pm 55 (\mathrm{stat.}) \pm 15 (\mathrm{sys.})$ Mpc, and $D_{\mathrm{V}} = 2031 \pm 22 (\mathrm{stat.}) \pm 10 (\mathrm{sys.})$ Mpc from the N+SGC power spectrum, bispectrum and simultaneous fitting, respectively.Comment: Submitted to Monthly Notices of the Royal Astronomical Society. 13 pages, 11 figure

Children at risk : their phonemic awareness development in holistic instruction

Includes bibliographical references (p. 17-19