Bibliographic record
Abstract
In a series of assignments spanning two semester-long courses, students propose research, collect data, and analyze findings to complete an ego-network-based study. Students are provided with a survey that uses several common methods of collecting ego network data (name generators, name interpreters, position generators, resource generators) and includes additional questions likely to be relevant to many topics. In the first semester, each students proposes a research project that can be completed by using the common survey provided and at most two additional survey questions to interview a convenience sample of undergraduate students. In the second semester, a survey is compiled from the original common survey and the additional survey questions that each student has proposed to add. Each student uses this survey to interview three undergraduate students. Students enter data from their interviews in an excel spreadsheet and submit the data to the instructor. The instructor compiles the submissions into a single data set and distributes this data to students. Students analyze these data to complete the research projects that they have proposed and write a paper based on their findings. The formatting of the survey is based on the formatting of surveys created by Keith Hampton, including a survey included in TRAILS resource #9796.
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 | 0.047 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".