Tag Archives: Council Watch

277 – Mediocrity at work.

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1000 words (11 minutes reading time) by Gordon Brittas

I seldom put pen to paper, however, reading the viewpoints of former colleagues has stimulated my thinking. Tim Whistler and Carole Parkinson’s posts on model collapse have started me thinking they are onto something. Is model collapse just mediocrity at work? Perhaps mediocrity is a product of model collapse? Or could it be a cause? So many questions.

As an expert in mediocrity, I must say that I know it when I see it.

Mediocrity

A recent paper by Tobias Jones entitled ‘Italy’s strangely seductive culture of mediocrity’, struck a chord with me. Jones cites a paper by the Oxford criminologist Diego Gambetta and Paris-based philosopher Gloria Origgi, ‘The LL game: The curious preference for low quality and its norms’, which discusses why so many academic conferences in Italy go seriously awry in a ‘cocktail of confusion, sloppiness, and broken promises’. It suggests that most Italians in academia prefer sloppiness to perfectionism.

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259 – Model Collapse in Local Government: A Performance Measurement Dilemma?

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700 words (8 minutes reading time) by Tim Whistler

In the world of artificial intelligence (AI), the concept of model collapse is a well-recognised challenge. Now it is potentially reaching into my everyday life if my Spotify-generated daylist starts to select songs from a previous daylist! Yet, my concern isn’t confined to personal playlists, and it should be echoing through the corridors of local government and raising questions about what performance measurement actually tells us.

Carole Parkinson’s question at the end of her footnote resonates with me: How can we detect model collapse in local government? It may well be a moot question, and model collapse is simply an explanation of the current situation, as one of Carole’s colleagues suggested.  This should become apparent if ChatGPT’s methods to identify model collapse in AI are accurate. Five seem particularly relevant for the local government context:

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