Identifying the Right Person in Social Networks with Double Metaphone Codes
Bibliographic record
Abstract
With English being considered as a universal language of communication and interaction, the study of the words that sound the same but spell differently has become an extensive area of research. In the context of social networks, people names can have different spellings yet the same pronunciation (i.e., homophonic names). Chris and Kris, John and Jon, as well as Justin and Justyn are some examples of these homophonic names. When exploring historical records, there may be situations in which two different entries correspond to the same individual, and the only difference in the two entries might be the spelling of the individual's name. Similar situations occur when individual's names are translated from their native language to another language. In this paper, we present a solution to identify the right person in social networks. The solution incorporates graph theory and linear algebra, and makes good use of double metaphone codes to measure the phonetic distance between two names. Based on the measured phonetic distance, homophonic names are grouped into the same cluster. This phonetic distance-based clustering helps to identify the right person in social networks.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".