Bibliographic record
Abstract
Tom King & Mitch Gerads' Mister Miracle (2017) has quickly become one of the most philosophically sophisticated superhero comics in recent memory, one that substantially departs from the classic representation of the title character and his supporting cast. A complex family drama, Mister Miracle asks the reader to grapple with questions that blur the lines between the "real" and the "imaginary", as well as what is "true" and "false". By applying the theoretical frameworks of Jean Baudrillard’s Simulacra and Simulation (1981), the obfusctation of Mister Miracle’s false reality can be revealed as a sophisticated simulacrum that utilizes (dis)simulation and elements of hyperreality as a means of control against Mister Miracle/Scott Free in order to wrench from him control of his “life”. Ultimately though, it becomes clear that this attempt to hijack and diminish Mister Miracle’s potential for a happy life results in the opposite of its intended effect, creating instead a paradoxical death that unintentionally validates the value of life through an alternate anti-life.
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 | 0.000 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".