Bibliographic record
Abstract
This study - Making the Fantastic Real - provides research into the subject of design fiction, science fiction fandom, sharable digital media content on the one hand, and the design of real-life engineering artifacts on the other. This study has selected the case of The Hacksmith to understand the space of meaning (science, fiction, co-creation, and design) created by the production of “the fantastic” by this specific acknowledged Canadian Youtuber. The Hacksmith Industries is the trademark name of the YouTube channel The Hacksmith (https://www.youtube.com/c/theHacksmith/featured) created by Canadian engineer James Hobson in 2006. One of the signature elements of Hobson’s interests is the lightsaber and the Star Wars franchise. But Hobson explores many other dimensions of popular culture, ranging from nerf wars shooters to superhero artifacts of the Marvel Cinematic Universe. His focus is on how to make elements or objects of the fantastic real, as his slogan goes, not to change any storyline, story arc or franchise world, but to see how far the innate human capability of play and creation can be taken from the fictional realm to reality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".