Bibliographic record
Abstract
Dune(s) Michel Pierssens, co-founder of SubStance (bio) Any great work of art, be it literary or otherwise, is made of intricate enigmas that admit infinite solutions, indifferent to their content, true or false, since no one holds the key (or Occam style razor) to judge, not even its author. In the best of cases, indeed, the author has produced his œuvre precisely to confront the unknown and face the deadly monsters that make the labyrinths worth exploring in the first place. The reader of the work, or watcher of the painting or film, cannot escape the perilous attraction of the cognitive maelstrom he, hesitantly or trustfully, enters at his own risk. Every word or stroke on canvas or chord or instant shutter of the camera or unending gros plan may hide unexpected doors to impossible worlds, each of them possible nonetheless. Love, hate, crime, lust, power, etc.: every facet of human experience can become scary or exciting forces to be faced, for just an instant or for an inconclusive lifelong struggle. Time is possibly the most intractable of those forces as only unconceivable gods can make of it their plaything. Similar to the desert planet of Frank Herbert's Dune, it harbors unpredictable monsters that destroy anything that moves—unless one learns to dance in an equally unpredictable fashion, winning a reprieve only to end up inescapably a fatality, having met the unavoidable fatum. SubStance has been travelling a much populated and cacophonic desert for fifty years, maintaining, despite the times' ubuesque twists and turns, its acrobatic démarche and tracing its empirical path through the intellectual dunes of two or three generations, collecting as it went many stimulating spices that it shared, avoiding its sale to the highest academic bidder. Editing a journal cannot be compared to the creation of a work of art, but it partakes in its aspiration to question the Known and face the Unknown, with the support of an army of minds devoted to the intriguing study of any cognitive coup de dés. [End Page 13] Michel Pierssens Michel Pierssens is co-founder of SubStance, with Sydney Lévy, and co-founder of the journal Histoires littéraires. He is Emeritus Professor of French literature, Université de Montréal. Copyright © 2023 Johns Hopkins University Press and SubStance, Inc.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.807 | 0.558 |
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".