30 Reminders for Sensitive People Who Feel Drained, Ashamed, or Judged

a post by Lori Deschene for the Tiny Buddha blog



There are some words that get painfully etched into our memories as if with a red-hot poker. For me, growing up, those words were “you’re too sensitive.”

I often caught this phrase in the fumbling hands of my shame after someone chucked it at me with callousness and superiority as a means to justify their cruelty.

They may have said something vicious or condescending in private, or told embarrassing stories or outright lies about me in public.

Either way, the results were the same: I’d take it personally, get emotionally overwhelmed, then either explode in anger or sob.

But it wasn’t just cruelty that evoked my sensitivity, and I didn’t cry only when obviously provoked.

Well-meaning people, who generally treated me with kindness, would gently remind me I’m too sensitive when I overanalyzed the smallest things other people did—like taking a while to call me back or “making a face” after I said something I thought sounded stupid.

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Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism

an article by Matt Carlson (Saint Louis University, USA) published in New Media & Society Volume 20 Issue 5 (May 2018)

Abstract

Journalistic judgment is both a central and fraught function of journalism.

The privileging of objectivity norms and the externalization of newsworthiness in discourses about journalism leave little room for the legitimation of journalists’ subjective judgment. This tension has become more apparent in the digital news era due to the growing use of algorithms in automated news distribution and production.

This article argues that algorithmic judgment should be considered distinct from journalists’ professional judgment.

Algorithmic judgment presents a fundamental challenge to news judgment based on the twin beliefs that human subjectivity is inherently suspect and in need of replacement, while algorithms are inherently objective and in need of implementation.

The supplanting of human judgment with algorithmic judgment has significant consequences for both the shape of news and its legitimating discourses.

Before I even glanced at the full text my head was reeling. Yes, humans make mistakes but not as often as a badly written algorithm which will get it wrong every time.

Full text (PDF 18pp)

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External examining: fit for purpose?

an article by Sue Bloxham (University of Cumbria) and Margaret Price (Oxford Brookes University) published in Studies in Higher Education Volume 40 Issue 2 (March 2015)

Abstract

In a context of international concern about academic standards, the practice of external examining is widely admired for its role in defending standards. Yet a contradiction exists between this faith in examining and continuing concerns about standards.

This article argues that external examining rests on assumptions about standards which are significantly open to challenge.

Six assumptions relating to the conceptual context, the operation and the nature of examiners themselves are analysed drawing on a review of the available evidence. The analysis challenges the notion of a consensus on standards and the potential to vest in individuals the ability to represent that consensus when judging the comparability of academic standards in a stable and appropriate way.

The issues raised have relevance to the UK and to other national systems using external examiners or seeking to guarantee academic standards by, in some cases, adopting quality assurance approaches developed in the UK.

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On the reliability of vocational workplace-based certifications

an article by H. Harth (Learning and Teaching, BPP Professional Education, London, UK) and B.T. Hemker (Cito, Arnhem, The Netherlands) published in Research Papers in Education Volume 28 Issue 1 (February 2013)

Abstract

The assessment of vocational workplace-based qualifications in England relies on human assessors (raters).

These assessors observe naturally occurring, non-standardised evidence, unique to each learner and evaluate the learner as competent/not yet competent against content standards.

Whilst these are considered difficult to measure, this study aims to provide information on the operational reliability of assessor decisions for three workplace-based vocational qualifications in Hairdressing and Electrotechnical Engineering.

It is shown how existing assessment records, typically generated locally by the training providers, were collected and used in a reliability study. A suitable methodology for estimating the inter-rater reliability and internal consistency was then used with this empirical data.

In general, the reliability estimates measured may be considered high.

This research study shows that it may be possible to measure reliability for these qualifications using commonly used measurement methods, although several characteristics of the assessment system should be considered in their interpretation.

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