The contextual name generator : a good tool for the study of sociability and socialization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it