Bibliographic record
Abstract
Distance plays a vital role in many applications of data analytics. In this paper, the concept of distance between any two data objects X and Y is addressed from the perspective of Shannon information theory. Consider a coding paradigm where X and Y are encoded into a sequence of coded bits specifying a codeword (or method) which would in turn convert Y into X, and X into Y such that both the distortion between X and X and the distortion between Y and Y are less than or equal to a prescribed threshold D. Given a class C of coding schemes within the coding paradigm, the information distance RC(X, Y, D) between X and Y at the distortion level D is defined as the smallest number of coded bits afforded by coding schemes from C. For two important classes C, RC(X, Y, D) is shown to be indeed a pseudo distance in some sense; it is further characterized or bounded. When C is the class of so-called separately precoded broadcast codes, it is shown that for any stationary, totally ergodic sources X and Y, RC (X, Y, D) is equal to the maximum of the Wyner-Ziv coding rate of X with Y as side information and the Wyner-Ziv coding rate of Y with X as side information. In the general case where C consists of all codes within the coding paradigm, upper and lower bounds to RC(X, Y, D) are established, and are further shown to be tight when X and Y are jointly Gaussian. The distance RC(X, Y, D) generalizes the notion of information distance defined within the framework of Kolmogorov complexity.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".