Bibliographic record
Abstract
A chief warning from George R.R. Martin is that the “night is dark and full of terror.” This article is a critical reflection on a moment from Martin’s masterpiece, A Song of Ice and Fire. Through a close reading of the series, I found that one of the most poignant moments is Daenerys Targaryen’s visionary journey through the House of the Undying. It was in this moment that I found the influence of the indelible Dante Alighieri. My article parallels Daenerys’s journey alongside Dante’s journey through hell in his Inferno. I begin by drawing a comparison between the warning Daenerys is given by Pyat Pree and the warning found outside of the gates of hell in Inferno. I finish by considering the prophecy of the three treasons experienced by Daenerys, mirroring Dante’s vision of Lucifer. While these experiences may seem dark, there are moments of light interspersed. Throughout this article, I also reflect on how Game of Thrones interprets Daenerys’s visions as prophecy. Ultimately, I conclude that the true story of “ice and fire” is the story that leads one from bondage to freedom in the ultimate search for truth.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".