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Right now, you can get in touch with me for a few things:
Freelance roles
Partnering on Side Projects
Talking to journalists
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Craig Hamilton

I am currently working for an EdTech scale up as a Data Coach. Prior to that I was a Research Fellow at a UK University. My research explored contemporary popular music reception practices and the role of digital, data and Internet technologies on the business and cultural environments of music consumption. This research was built around the development of The Harkive Project (www.harkive.org), an online, crowd-sourced method of generating data from music consumers about their everyday relationships with music and technology. I was also the co-Managing Editor of Riffs: Experimental Research on Popular Music (www.riffsjournal.org) and the Project Co-ordinator on the AHRC-funded Songwriting Studies Network. 

Outside of work, I continue to build on my 20+ years of working in the business of popular music, working as digital catalogue manager for Static Caravan Recordings and as a musician and recording artist with Independent Country. I live in Birmingham, England, with my wife and sons and an unruly dog, and when not working I enjoy collecting records, following Aston Villa, and developing skills related to data science.
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I'm available for
2022
Mar 29, 2022
Mar 29, 2022
Last week I was approached to provide some comment for this article in Input about a new NFT-Vinyl service. 

https://www.inputmag.com/culture/vinyl-records-nfts-moses-sumney-king-gizzard-lizard-wizard-vinylkey
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Mar 27, 2022
Mar 27, 2022
In my role as 1/6 of the Birmingham-based Country Rock band, Independent Country, I’m delighted to say that we have a new EP out today. Here’s the blurb from the band’s website:

Our latest release hits the shelves today. It’s a 4-track EP of tracks from the American Portions album sessions, and a companion piece to that record.

We kick off with a different, slightly quicker version of REM‘s Shiny Happy People to the one that appeared on the May 2021 album, and then we change gears again for a breakneck race through She Don’t Use Jelly by The Flaming Lips. Next up is a tear-jerker, Death Cab For Cutie‘s I Will Possess Your Heart, before we finish up with an 11-minute (!) ambient country ride through the desert with Queens of the Stone Age‘s Feelgood Hit of the Summer. 

The EP is available on all streaming services and as a CD and download from our Bandcamp page.

https://independentcountry.bandcamp.com/album/american-portions-side-orders

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Mar 27, 2022
Mar 27, 2022
I recently wrote journal article with a colleague, and the research behind it has involved the collection and analysis of data related to music podcasts. During the course of undertaking this work, I have developed a workflow able to gather data for ~9,000 podcast episodes and ~18,000 reviews from the input of a single URL. The post below presents that process as a replicable workflow.

https://www.popmusicresearch.org/post/rate-review-partone/
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Mar 27, 2022
Mar 27, 2022
I recently came across Julia Silge’s excellent online course around the use of the TidyModels package in R, and from there to the suite of resources on the dedicated TidyModels website. Both contain short exercises the demonstrate the basics of the package. I decided to create a small-scale project around that learning that also combined some other skills and techniques I had previous picked up.

I came up with the idea of a project that would gather data from the Spotify API, perform some EDA (exploratory data analysis) and visualisation, and then use that as the basis for a simple Logistic Regression model that could make predictions about the relative age of songs. I then produced a small Shiny app that would enabling users to interact with the model by entering songs to see the results of predictions.

The model was trained on ~5,000 hit songs recorded between 1970 and 2019. The aim was to classify songs as being from either before or after the mid-point year of 1995. The model uses metrics available from Spotify’s API - including tempo, duration, ‘valence’, ‘acousticness’, etc. - to make predictions. You can see a live, working version of the app here.

In testing, the model achieved ~85% accuracy but tended to struggle most with songs recorded in years immediately either side of 1995. It also has difficulty with songs that exemplify certain genres (e.g. jazz, country), so it can produce some odd predictions! In the main, however, it seems to be pretty accurate – although the ‘fun’ in playing with it is largely in finding songs that fool it. In truth, the aim of the process was not to create a model that worked 100% of the time – and, in any case, who really needs to know if a song is from before or after 1995? – rather, it was to see how the TidyModels package could be incorporated into a broader workflow that might be useful to students and other people learning some fundamental skills with R. I think the workflow hangs together quite nicely, and makes for a nice mini-project.

See more here: https://www.popmusicresearch.org/post/spot-model/
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Mar 27, 2022
Mar 27, 2022
A journal article I co-wrote last year on how live music is measured and valued is currently available Open Access.

https://bit.ly/36MYnBH
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Mar 27, 2022
Mar 27, 2022
I'm excited to check out Polywork as one of the first early adopters. 🔥 😎 🤖
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