Bibliographic record
Abstract
Fernando Deecy (Andrew Brown) is haunted by hallucinations of a dead friend (Tristan Coyle) as he aspires to become a filmmaker. Sean Sanders (Gareth McCall) is on the run from crazed Cuban drug lord Tony Romano (Alex Antonio). Sergeant Bilkov (Stefan Brown)... well, nobody really cares about *him*. The point is, one way or another they all somehow become members of 666 Hijack Squadron of the Royal Canadian Air Cadets, where they are separated into teams and engaged in a fierce competition to produce the best educational film about cadet harassment and abuse prevention ever seen. The second full-length feature film by Stefan Brown, Fernando's Hideaway is much longer and more complicated than originally visioned... thus making it much funnier as well. It also features the first appearance by Alex Antonio in a Fester House film, and the performance that made him a permanent staple in our cast lineup. Keep an eye out for references to everything from Kill Bill to Groundhog Day, and be sure to watch the bloopers (the smaller of the two DivX files) for even more chuckles. Oh, and for those leery of the filesize, trust me: I tried making it 500 megs and what I saw was clearly not meant for mortal eyes.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.323 | 0.075 |
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".