The diverse origins of neutron-capture elements in the metal-poor star HD 94028 : possible detection of products of i-process nucleosynthesis

We present a detailed analysis of the composition and nucleosynthetic origins of the heavy elements in the metal-poor ([Fe/H] = −1.62 ± 0.09) star HD 94028. Previous studies revealed that this star is mildly enhanced in elements produced by the slow neutron-capture process (s process; e.g., [Pb/Fe] = +0.79 ± 0.32) and rapid neutron-capture process (r process; e.g., [Eu/Fe] = +0.22 ± 0.12), including unusually large molybdenum ([Mo/Fe] = +0.97 ± 0.16) and ruthenium ([Ru/Fe] = +0.69 ± 0.17) enhancements. However, this star is not enhanced in carbon ([C/Fe] = −0.06 ± 0.19). We analyze an archival near-ultraviolet spectrum of HD 94028, collected using the Space Telescope Imaging Spectrograph on board the Hubble Space Telescope, and other archival optical spectra collected from ground-based telescopes. We report abundances or upper limits derived from 64 species of 56 elements. We compare these observations with s-process yields from low-metallicity AGB evolution and nucleosynthesis models. No combination of s- and r-process patterns can adequately reproduce the observed abundances, including the super-solar [As/Ge] ratio (+0.99 ± 0.23) and the enhanced [Mo/Fe] and [Ru/Fe] ratios. We can fit these features when including an additional contribution from the intermediate neutron-capture process (i process), which perhaps operated through the ingestion of H in He-burning convective regions in massive stars, super-AGB stars, or low-mass AGB stars. Currently, only the i process appears capable of consistently producing the super-solar [As/Ge] ratios and ratios among neighboring heavy elements found in HD 94028. Other metal-poor stars also show enhanced [As/Ge] ratios, hinting that operation of the i process may have been common in the early Galaxy

Generalising from Qualitative Research (GQR): A New Old Approach

In this paper, the authors debunk a long-held myth that generalisation is primarily the domain of quantitative research. Based on a review of modern and historical approaches to generalisation, they argue that generalisation from qualitative research (GQR) can be achieved, not through a process of self-justification, but through defensible and rigorous research design and methods. The authors go on to consider examples from their own qualitative research work spanning the last 20 years. From these examples they offer mechanisms that qualitative researchers can employ to generalise from their findings. They suggest that generalisation is achieved through a process of generalisation cycles (GCs) which produce normative truth statements (NTSs), which in turn can be contested or confirmed with theory and empirical evidence

Designed Generalization from Qualitative Research

In our earlier work on generalizing from qualitative research (GQR) we identified our two-decade struggle to have qualitative research outcomes formally “listened to” by policy personnel and bureaucratic systems in general, with mixed success. The policy sector often seems reluctant to acknowledge that qualitative research findings can be generalized, so impacts tend to be informal or simply ignored. The “official” methodological literature on generalizing from qualitative research is epitomized by Lincoln and Guba’s (1985) still oft quoted, “The only generalization is: there is no generalization” (p. 110). We now understand there are many alternative possibilities for generalizing. In this paper we hope to provide a platform for discussion on GQR. We suggest Normative Truth Statements (NTS) as a foundation. NTSs, used in our proposed generalizability cycle, are a potential key to ensuring designated qualitative research methodology provides a capacity for generalization—and therefore be considered as a valid form of evidence in policy decisions. In other words, we need a platform to articulate how to design qualitative research to maximize the type and scope of generalizability outcomes, referred to here as Designed Generalization from Qualitative Research (DGQR). Five steps of DGQR, using progressive NTSs in the generalizability cycle, are proposed as a way forward in understanding how generalizing from qualitative research may be made more transparent, accountable, and useful. The five steps are illustrated by reference to two example studies