SleepeR (incomplete)

What is it A BASIC Blurb version TRY AND EXPLAIN (Lesia might need to help me)


The original version of this idea come out from my own PhD and the rough draft can be read on Manifold followed by the funded application too Manifold. The pilot funding came from the Web Science Institute (WSI) to support creating a Sleeper proof of concept. I then have to look to apply for external funding when I report back on the pilot to the WSI in the summer of 2023.

So, I have assembled a small team of interested and useful individuals, Lesia Tkacz, Ash Ravi and Maddie Dwyter, who are helping to make and test this proof of concept.

Our roles are;

Adam Procter: Project Lead, this means I am responsible for the project and leading the ideas and also working directly with students/staff in

Lesia Tkacz: Product Manager, EXPLAIN

Ash Ravi : NLP Research Engineer, EXPLAIN

Maddie Dwyer : Human Interface Designer, EXPLAIN

The project team are using internal Slack and not our discord for now as we are all in this tool more often daily, but the broader discussion and output will be published and made open via this series of blogs, the report and likely some things into discord alongside my Obsidian notes on Gitlab.

The code is also all being published openly on GNU AFFERO GENERAL PUBLIC LICENSE Version 3 alongside all my Obsidian notes that I am taking randomly as the project progresses. Gitlab Group.

Package 1

In the first week, Lesia helped to extract the selected texts we had gathered to support the TOY project as a mini reading list into readable txt files, we could use as our CORPUS. We also agreed some internal terms to help organise ourselves.

CORPUS = texts, journals, and books from the “reading list”

DOCUMENT = the selected text from the CORPUS

KEYWORDS = words extracted from the JSON file from each team’s microcosm nodes

EXTRACT= span of text in the document that contains the keywords

For our first CORPUS we brought in these initial set

  • Excerpt from Roland Barthes from Mythologies on Toys
  • Reay, E. (2022) ‘Immateriality and Immortality: Digital Toys in Videogames’, Playful Materialities
  • Giddings, Seth (2019) ‘Toying with the singularity: AI, automata and imagination in play with robots and virtual pets’, in Giovanna Mascheroni & Donell Holloway (eds) The Internet of Toys: practices, affordances and the political economy of children’s smart play. Palgrave Macmillan.
  • Heljakka, K. (2017) ‘Toy fandom, adulthood, and the ludic age: creative material culture as play’, Fandom, Second Edition: Identities and Communities in a Mediated World, edited by Jonathan Gray, Cornel Sandvoss and C. Lee Harrington, New York, USA: New York University Press, 2017, pp. 91-106.
  • Blasdel, A. (Nov. 2022), ‘They want toys to get their children in to Harvard’: have we been getting playthings all wrong?’, The Guardian,

Lesia then used an existing programme called AntConc to test some ideas on how we might take keywords and do a compare with the CORPUS. The overall suggestion was to start simple and start with TF-IDF (term frequency-inverse document frequency)

Package 2

Now I was set to gather some real keyword data. So, in the studio, I explained the project of to year 1 Games Design & Art students and the concept that SleepeR would recommend readings from the CORPUS. Each student was then placed in their teams after a field trip to Legoland, to work collectively on idea gathering, using and its Collect view. I ran three sessions in one day.

These exercises using included;

Exercise 1: create single nodes with keywords, as many keywords they could each think of per team specifically on the Legoland field trip.

Exercise 2: write together in nodes thoughts and ideas on the 5 emotions chosen for the thematic under pinning of TOY. EMOTIONS HERE

Exercise 3: use to think out loud and gather general ideas and visuals related to Toys, Craft and Textiles, one member of the team had to visit the library and bring back physical items.

While this was underway, Ash was making the concept as outlined by Lesia into a small Python programme. The programme would take the keywords (JSON) from exercise 1 i the studios within, and use the CORPUS to do a TF-IDF lookup and provide each team with the “top” DOCUMENT.



storytelling 0.415010

combining 0.415010

land 0.207505

inside 0.207505

message 0.207505




welcome 0.367053

dreamer 0.367053

audience 0.367053

player 0.296136

education 0.296136

'Reay Digital Toys.txt'



welcome 0.311668

themed 0.311668

curiosity 0.311668

whimsical 0.311668

dreamer 0.311668

‘Reay Digital Toys.txt’



welcome 0.562550

system 0.376746

created 0.376746

color 0.281275

creepy 0.281275




shopping 0.368065

welcome 0.368065

realworld 0.368065

educational 0.296953

holiday 0.296953




stimulation 0.369146

welcome 0.369146

dragon 0.297824

build 0.297824

lego 0.247221




welcome 0.396977

unique 0.396977

target 0.396977

audience 0.396977

big 0.396977

‘heljakka toy fandom.txt’


lego 0.596953

shop 0.297119

creativity 0.198984

park 0.148560

rest 0.148560


After the teams had all had their top DOCUMENTS returned, we did a small survey and of all students that responded they said most had not read any of the DOCUMENTS in the CORPUS and that all were now more likely to read the DOCUMENT than before.

Package 3

Next we discussed what make this more useful, and to perhaps provide a view into the CORPUS maybe taking the “top” DOCUMENT and providing detail such as an EXTRACT.

We also added to our CORPUS two more DOCUMENTS from out broader reading list.

  • Rules of play: game design fundamentals
  • The art of game design: a book of lenses