The contextual name generator : a good tool for the study of sociability and socialization
Bibliographic record
Abstract
The debate on the relative validity, power, limits and relevance of different name generators \nhas evolved in line with the development of the social network studies. The core questions are: \nwhat do they respectively refer to? What are they supposed to construct, for what research \nquestion? Some procedures tend to choose a precise target with a unique name generator that \nmay synthesize a crucial point. Others prefer to use series of different name generators, in order \nto gather names referred to diverse spheres of social life. In this case the various name \ngenerators are often built with heterogeneous logics, and often remain incompatible. \nIs it possible to standardize a procedure to truly overcome these limits and keep the \ncomparisons possible? We discuss here some specificities and advantages of a new kind of \nintegrated name generator, the “contextual” name generator, which was developed in a \nlongitudinal qualitative panel study that started in France in 1995 and was also conducted in \n2005 in three different projects in Quebec. This tool is not the juxtaposition of independent \nname generators, as we are used to; it combines their respective advantages in a real integrated \nand systematic procedure and allows going through a wide range of areas, scales, social \nconditions, qualities of ties, etc. This name generator gives access to a great diversity of \ninformation that allows to combine sociability and socialization questions. It thus seems to be a \nrelevant tool, especially for sociologists.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".