Bibliographic record
Abstract
Traditional computer languages are all ultimately based on mathematical logic, which, after all, is the foundation of practically all of modern mathematics. This is a natural outcome of the initial algorithmically-oriented applications of electronic computing machines and is even revealed in how we've chosen to name these devices (i.e., computers). One obvious aspect of this is reflected in the fact that values in programs are typically represented by data types, such as integers, reals, or strings, which are quite intentionally shorn of any connotations. Consequently, in cases where such data is intended to represent relevant quantities, such as length or communication bandwidth, the association with the corresponding dimensions is typically informal, through convention. This has led to some catastrophic and expensive failures, such as the case of the unfortunate Mars Lander spacecraft, which was attributed to an undetected mismatch between metric and imperial systems measures. The informal nature of the association between values expressed in programs and their corresponding dimensions can also greatly complicate proper verification of such software. Whereas a great deal of effort has been expended in evolving various type theories for computer languages in order to avoid mismatches between pure data types, very little has been done to help us with problems with physical data types. In the past, this was perceived as a concern primarily for the relatively specialized field of real-time computing. However, as more and more software involves interactions with the world, this deficiency is becoming more obvious, more pervasisve, and more critical. Thus, with the growth of the Internet, many modern software systems are physically distributed and, consequently, highly sensitive to phenomena, such as communication delays, equipment failures, out-of-sequence events, and the like. In other words, more and more software is becoming real-time. In this talk, we focus on the issues involved in the somewhat contradictory relationship between the orderly logical world of traditional software and the complex and sometimes unpredictable world with which it interacts. Specifically, we look at how computer languages should be constructed to deal more effectively with this complex combination.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".