The Sunbelt 2013 Data: Mapping the Field of Social Network Analysis
Bibliographic record
Abstract
Title Title of the paper presentation Author(s) Author(s) of the submission. The presenter is underlined; superscript numbers connect people to institutions in case there is more than one institution involved. Institution(s) Institution(s) of the author(s); superscript numbers connect to author(s). Country Country of the person that submitted the abstract. The person doing the submission is not necessarily the presenter or the first author. Session Title Title of the session in which the paper was presented. This is the assigned session (see section 2.2.) not the session topic suggested by the author(s). Session Code Day/Time slot and room ID describing when and where the paper was presented. Talk Nr One session consists of multiple talks (normally five or six). This number indicates the position of the paper presentation within the session. keywords have been selected (avg. 3.76) for all 749 paper and poster presentations. Every single keyword was used at least two times. The top used keywords are Social Capital, Egocentric Networks, and Inter-organiza-tional Networks. The columns of the keywords table are defined as follows. ID Submission identifier Type Paper or poster presentation Keyword Keyword selected from a pre-defined list of keywords
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.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".