Bibliographic record
Abstract
Amidst the parched and hazy drought and wildfire season in Alberta’s Rocky Mountains and prairies, an AI start-up approaches a famous Instagram influencer to embed her likeness into its avatars. Set beyond the AI’s murky digital landscape, “The Underscore” explores the journey of four characters with interwoven narratives during two critical junctures: the proliferation of AI friendship and the dangers of a looming forest fire in Jasper. Sophie_Grace is a lauded millennial known for her social media empire who chooses to ignore the warning signs of her AI. Sophie’s best friend Iris risks her personal and professional connections despite her reservations and defends Sophie’s decisions until one event threatens to crater their relationship permanently. Rowan, Iris’s partner, is a Woodlands firefighter and quietly observes the changing social and climate environments before him, until a spark ignites a blaze that threatens to ravage Jasper. Annie is a young high-schooler enamoured with the Sophie_Grace. When Sophie_Grace uploads her personality into AI, Annie jumps at the chance to have realistic conversations with the famous influencer. A speculative work of realist fiction, “The Underscore” examines the pernicious elements of artificial intelligence through the very human and messy nature of genuine connection, heartache, and humour.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".