Reliability of Sequence-Alignment Analysis of Social Processes: Monte Carlo Tests of Clustalg Software
Bibliographic record
Abstract
Sequences of characters are used in many fields to record events or processes that characterize social processes. However, until recently, there have been very few methods available for the analysis of character-sequence data. Alignment algorithms measure similarities between pairs of sequences by inserting gaps into one or the other to create the best possible matching pattern. In this paper the reliability of alignments in the classification of sequential data is examined. Alignment methods were developed in computational biology, but are being considered for applications in other fields such as sociology, geography, and transportation planning. The ClustalG multiple alignment package is used to examine a set of synthetic sequences generated through the use of eight separate generation rules. Through the application of the software to sequential data with a known number of subgroups and known patterns in the sequences, some strategies for conducting the analysis can be compared and evaluated. The most effective strategy for analysing sequential data when the underlying processes that generate the event sequences are not known is to use low gap penalties that permit the maximum numbers of matches.
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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 | 0.125 | 0.441 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".