Bibliographic record
Abstract
Colin Harrison's novel The Finder (2008) uncovers the hidden hierarchy of differential information. We live in a knowledge economy, or so they say. And in the world of finance, knowledge is power: the power to buy assets before their price appreciates. This knowledge-as-power, though, is profitable only when exclusive. Common knowledge – no matter how sophisticated and complex – is never profitable. Only differential knowledge – i.e., knowledge that is unavailable to others or superior to what they have – can yield a ‘return’. This differential prerequisite explains why every entity in the pyramid of financial information – whether a person or an organization – has no more than a partial vista, with the remaining view blurred by enforced opaqueness and power-backed misinformation. The different vistas are also deeply formative. Individual ‘actors’ may feel empowered by what they know, but in practice, what they know serves to frame their thoughts and direct their actions – usually without them ever knowing it. Even those at the very top – indeed, especially those at the very top – are slaves to their knowledge, however superior.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".