Almost a month has passed since I last blogged (busy with other things rather than avoiding study, honest) and this week I planned to throw myself back into the assignment, having closed off most of the other distractions for now.
The idea was to get out to one or more of the remaining locations (Dewsbury, Barnsley and my current home of Pickering) for full days of shooting – but the weather forecast is for persistent rain in all three locations for the rest of the week. I know bad weather shouldn’t be a deal-breaker but having wasted a rainy day in Accrington early on in this assignment I think it’s worth waiting slightly longer for drier weather.
So even though it sometimes feels like I’ve been doing too much planning and not enough shooting, I intend to use at least some of this week to refine my shooting plan per town – not in such a way that restricts me, more in a way that amplifies the points I’m aiming to make.
To recap, the concept is to juxtapose pairs of images of specific northern English towns based on stereotypes / clichés of how the population voted in the 2016 EU Referendum – as a comment on the absurdity of extreme over-simplification.
The visual treatment is based on the images being in the proportions of the Remain/Leave vote ratio so the images will resemble infographics to some degree.
The text labelling is key – I will be using pairs of increasingly provocative labels to highlight the extent to which we tend to generalise about populations.
The list I brainstormed a while ago is as follows, though here I have reordered it to build up from neutral/innocuous to more judgemental/offensive, to give a loose narrative arc (or at least a sense of escalation):
- Remain / Leave
- Globalist / Nationalist
- White Collar / Blue Collar
- Young / Old
- Urban / Rural
- Rich / Poor
- Have / Have Not
- Multicultural Middle Class / White Working Class
- Upwardly Mobile / Down and Out
- Metropolitan Elite / Left Behind
- Establishment / Workers
- Enemies of the People / The People
- Strivers / Skivers
- Foreigners / Racists
- _____ / _____ (I intend to leave the labels blank on the last pairing, with the implication that the viewer can make up their own stereotypes)
Some of these lend themselves to particular towns more than others and so I will look to group them accordingly:
- Young / Old and Urban / Rural are most appropriate for Pickering, which has notable extremes of both
- The Establishment / The Workers could work best in Middlesbrough or Barnsley as both have experienced notable industrial decline in recent times
- Metropolitan Elite / Left Behind aligns well with Burnley as it has examples of both extremes
- Foreigners / Racists (undoubtedly the most provocative pairing) will work best in Dewsbury which has a high ethnic minority population
The challenge I’m setting myself whilst I wait for better weather is to think of associations with these words that might lead to subject ideas. Again, I don’t mean this to be prescriptive but to open up some neural pathways :-)
I want to see if I can work in some metaphors and metonyms that allude to the labels in some way; I don’t mind if they are obscure, as it’s mostly for my own inspiration that I wanted to do this word association thing.
- Straight road
- Exit sign
- Travel agents
- Union jack
- White Collar
- Blue Collar
- Working men’s club
- Micro scooter
- Mobility scooter
- Wine bar
- Farm shop
- Car dealership
- Bus stop
- Have not
- Phone box
- Multicultural Middle Class
- Coffee shop
- White Working Class
- Chip shop
- Upwardly Mobile
- New build
- Down and Out
- Derelict building
- Metropolitan Elite
- Left Behind
- Food bank
- Council offices
- Enemies of the People
- The People
- Shopping centre
To be realistic it’s very unlikely (and overly limiting) that I’ll be using this as a subject checklist while I shoot – the value in this exercise was simply to expand my horizons on potential subject matter.
In parallel with this text brainstorming, I’m also spending some time this week looking at how other photographers have captured places, specifically English towns, without relying too much on pictures of people. A separate research post on this will follow shortly.