Justice in Rational Transmission
Bibliographic record
Abstract
In Chapter 1 we formulated a rational system for transmission. In Chapters 4 and 5 we integrated rationality and cooperative action to show how disproportionately large benefit gains for quantizers and strong oscillations in gain at very low bit rates can be prevented. In this last chapter we study the quantizer formation to provide a just approach for rational transmission. Figure 6.1 summarizes the relationship among rationality, cooperative action, and justice in the problem of progressive transmission. In a rational system for transmission, a discrete wavelet transform provides a representation of the original image, and the SOT naturally defines the spatial relationship in the pyramid that results from the transformation. Each node of the tree corresponds to a pixel, and its direct descendants (offspring) correspond to the pixels of the same spatial orientation in the next finer level of the pyramid. Transform coefficients in an SOT correspond to a particular region of the original image, and thus, each SOT is associated with one spatial region. Individual SOTs may be grouped together to form a reduced number of quantizers that convey structural information about the picture to the rational transmission. A prioritization protocol, whereby the order of importance is determined by means of a rational approach, involves a choice at each truncation time among alternative quantizers for further transmission in such a way as to avoid certain forms of behavioral inconsistency, as described in Chapter 1.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".