Reviews of Daniel Davis\u27s Contingent Academic Labor and Lisa del Rosso\u27s Confessions of an Accidental Professor

This review covers Daniel Davis\u27s Contingent Academic Labor: Evaluating Conditions to Improve Student Outcomes and Lisa del Rosso\u27s Confessions of an Accidental Professor. Davis\u27s book offers a rubric for evaluating the working conditions of contingent academic laborers. del Rosso\u27s Confessions is a memoir of her experience as a contingent academic laborer

Controlling the Bureaucracy of the Antipoverty Program

Rapid progress made in various areas of regenerative medicine in recent years occurred both at the cellular level, with the Nobel prize-winning discovery of reprogramming (generation of induced pluripotent stem (iPS) cells) and also at the biomaterial level. The use of four transcription factors, Oct3/4, Sox2, c-Myc, and Klf4 (called commonly "Yamanaka factors") for the conversion of differentiated cells, back to the pluripotent/embryonic stage, has opened virtually endless and ethically acceptable source of stem cells for medical use. Various types of stem cells are becoming increasingly popular as starting components for the development of replacement tissues, or artificial organs. Interestingly, many of the transcription factors, key to the maintenance of stemness phenotype in various cells, are also overexpressed in cancer (stem) cells, and some of them may find the use as prognostic factors. In this review, we describe various methods of iPS creation, followed by overview of factors known to interfere with the efficiency of reprogramming. Next, we discuss similarities between cancer stem cells and various stem cell types. Final paragraphs are dedicated to interaction of biomaterials with tissues, various adverse reactions generated as a result of such interactions, and measures available, that allow for mitigation of such negative effects

Redressing grievances and complaints regarding basic service delivery

Redress procedures are important for basic fairness. In addition, they can help address principal-agent problems in the implementation of social policies and provide information to policy makers regarding policy design. To function effectively, a system of redress requires a well-designed and inter-linked supply of redress procedures as well as, especially if rights consciousness is not well-developed in a society, a set of organizations that stimulate and aggregate demand for redress. On the supply side, this paper identifies three kinds of redress procedures: administrative venues within government agencies, independent institutions outside government departments, and courts. On the demand side, the key institutions are nongovernmental organizations/civil society organizations and the news media, both of which require a receptive political and economic climate to function effectively. Overall, procedures for redressing grievances and complaints regarding basic service delivery are under-developed in many countries, and deserve further analysis, piloting, and support.Public Sector Corruption&Anticorruption Measures,Corruption&Anticorruption Law,Public Sector Regulation,Health Monitoring&Evaluation,Governance Indicators

Automatically detecting open academic review praise and criticism

This is an accepted manuscript of an article published by Emerald in Online Information Review on 15 June 2020. The accepted version of the publication may differ from the final published version, accessible at https://doi.org/10.1108/OIR-11-2019-0347.Purpose: Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research’s open peer review publishing platform. Design/methodology/approach: PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings. Findings: PeerJudge can predict F1000Research judgements from negative evaluations in reviewers’ comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision. Originality/value: PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments