Bibliographic record
Abstract
Abstract We define teleonometrics as the theoretical and empirical study of teleonomy. We propose three laws for teleonometrics. The first law describes the hierarchical organization of teleonomic functions across biological levels from genes to individuals. According to this law, the number of goal-directed functions increases from individuals (one goal, maximizing inclusive fitness) to intermediate levels and to genes and alleles (myriad time-, space- and context-dependent goals, depending upon degrees and patterns of pleiotropy). The second law describes the operation of teleonomic functions under trade-offs, coadaptations and negative and positive pleiotropies, which are universal in biological systems. According to this law, the functions of an allele, gene or trait are described and defined by patterns of antagonistic (trading off) and compatible (coadapted) functions. The third law of teleonometrics is that the major transitions in evolution are driven by the origins of novel, emergent goals associated with functional changes and by the breaking and reshaping of trade-offs, especially by mechanisms involving increases in resources or time, and new divisions of labour or function. We illustrate the application of these laws using data from three empirical vignettes, which help to show the usefulness of teleonometric viewpoints for understanding the interfaces between function, trade-offs and dysfunctions manifest as disease.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".