Revising: Drafts #2 Through…

This chapter discusses the revision process. Particular emphasis is placed on revision, with both the statistical argument and intended audience in mind. The chapter provides advice for revising effectively, including strategies for targeted revision. Example guideliness for both giving and receiving feedback during the peer-review process are included.

The FRB–SGR connection

ABSTRACT The discovery that the Galactic Soft Gamma Repeater (SGR) 1935+2154 emitted Fast Radio Burst (FRB) 200428 simultaneous with a gamma-ray flare, demonstrated the common source and association of these phenomena. If FRB radio emission is the result of coherent curvature radiation, the net charge of the radiating ‘bunches’ or waves may be inferred from the radiated fields, independent of the mechanism by which the bunches are produced. A statistical argument indicates that the radiating bunches must have a Lorentz factor ⪆ 10. The observed radiation frequencies indicate that their phase velocity (pattern speed) corresponds to Lorentz factors ⪆ 100. Coulomb repulsion implies that the electrons making up these bunches have yet larger Lorentz factors, limited by their incoherent curvature radiation. These electrons also Compton scatter the soft gamma-rays of the SGR. In FRB 200428, the power they radiated coherently at radio frequencies exceeded that of Compton scattering, but in more luminous SGR outbursts, Compton scattering dominates, precluding the acceleration of energetic electrons. This explains the absence of a FRB associated with the giant 2004 December 27 outburst of SGR 1806−20. SGR with luminosity ≳ 1042 erg s–1 are predicted not to emit FRB, while those of lesser luminosity can do so. ‘Superbursts’ like FRB 200428 are produced when narrowly collimated FRB are aligned with the line of sight; they are unusual, but not rare, and ‘cosmological’ FRB may be superbursts.

Collective learning in news consumption

In a complex digital space---where information is shared without vetting from central authorities and where emotional content, rather than factual veracity, better predicts content spread---individuals often need to learn through experience which news sources to trust and rely on. Although public and experts' intuition alike call for stronger scrutiny of public information providers, and reliance on global trusted outlets, there is a statistical argument to be made that counter these prescriptions. We consider the scenario in which news statements are used by individuals to achieve a collective payoff---as is the case in many electoral contexts. In this case, a plurality of independent though less accurate news providers might be better for the public good than having fewer highly accurate ones. In a carefully controlled experiment, we asked people to make binary forecasts and rewarded them for their individual or collective performance. In accordance with theoretical expectations, we found that when collectively rewarded people learned to rely more on local information sources and that this strategy accrued better collective performance. Importantly, these effects positively scaled with group size so that larger groups benefited more from trusting local news sources. We validate these claims against a real-world news dataset. These findings show the importance of independent (instead of simply accurate) voices in any information landscape, but particularly when large groups of people want to maximize their collective payoff. These results suggest---at least statistically speaking---that emphasizing collective payoffs in large networks of news end-users might foster resilience to collective information failures.

Leisure mobility and individuals’ relationship to the living environment: a comparison between the Paris and Rome urban regions.

In this article we examine how (alongside with other factors) the relationship that individuals have to their living environment affects their leisure mobility. We first elaborate a typology (comprising 5 types) of individuals according to their stated relationship to their living environment. Using a statistical approach, we then show that this typology partially explains inter-individual differences in leisure mobility, after taking into account other socioeconomic and spatial explanatory factors: income, level of education, profession, residential location (esp. density of residential area) and demographic characteristics. This statistical argument is complemented with a qualitative study of the meanings given by individuals to their living environments and leisure mobility practices, which ultimately contributes to better understand the drivers of leisure mobility and to emphasize in particular the notion of compensatory mobility. A given urban context may accomodate very different practices and very diverse life projects and the approach developed in the paper has allowed to move away from deterministic explanations for leisure mobility.

A Note on the Folly of Cross-Sectional Operationalizations of Generations

Costanza and Finkelstein (2015) have justly argued that cross-sectional operationalizations of generational groups represent a confound that constrains the ability to unequivocally separate the effects of age and period. A related statistical argument against this practice bears consideration as well. Namely, cross-sectional operationalizations of generations have the potential to unduly inflate type two-error rates when compared with the analysis of simple age effects. This is a problem because true age effects can be erroneously ignored in studies where age is artificially split into assumed generational groups. Indeed, the argument against artificially bifurcating continuous data is not new (e.g., Cohen, 1983), however past attempts to make inferences about generational effects in cross-sectional designs present an opportunity to investigate the particularly insidious nature of this practice and its implications. To demonstrate the problem at hand, let us consider a brief empirical example by virtue of a simulation study.

When is an ecological network complex? Connectance drives degree distribution and emerging network properties

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

When is an ecological network complex? Connectance drives degree distribution and emerging network properties

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

When is a network complex? Connectance drives degree distribution and emerging network properties

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

When is a network complex? Connectance drives degree distribution and emerging network properties

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.