Bibliographic record
Abstract
Functioning as re re real antiontology, viral amnesia machinically realizes and dissolves biological […] cultural, and technical 010110100100 […] mnemic structures: chopping-up hierarchic-generational descendency, collapsing phylogenetic tic frozen-code into ontogeny, and immanentizing the past to operative current.-Nick Land, 'Hypervirus', 2011. Bootstrapping Virality'Go Viral!' says the cover of the latest issue of the UK edition of Wired (Feburary, 2014) that I picked up at Montreal airport on my way to Tokyo to attend the 'Life under Influence' seminar.Inside was a five-page long article on Buzzfeed, the 'viral lab' that 'mastered social sharing (and GIFs and kittens) to become a media giant for a new era' (Rowan 2014).But within the article, I could not find any mention of the word 'virus'-much less a definition of this entity-only its adjective form repeated over and over, and a mere reference to 'memes', the word that Richard Dawkins coined 40 years ago to refer to 'mind viruses' (Dawkins 1989(Dawkins [1976]]).Think Grumpy cat.But this, I am afraid, will not help me make my point here.'Life is a chance' 92,300,000 'Life is a gift' 59,700,000 'Life is a process' 28,100,000 'Life is a product' 27,600,000 'Life is a challenge' 26,100,000 'Life is a solution' 22,900,000 'Life is a disease' 9,920,000 'Life is change' 2,860,000 'Life is shit' 2,270,000 'Life is a dream' 926,000 'Life is a journey' 902,000 'Life is a game' 319,000 'Life is code' 16,400Hence: 'Life is paradoxical' 68,700
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".