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
Abstract Broadly speaking, cybervetting can be described as the acquisition and use of online information to evaluate the suitability of an individual or organization for a particular role. When cybervetting, an information seeker gathers information about an information target from online sources in order to evaluate past behavior, to predict future behavior, or to address some combination thereof. Information targets may be individuals, groups, or organizations. Although often considered in terms of new hires or personnel selection, cybervetting may also include acquiring and using online information in order to evaluate a prospective or current client, employee, employer, romantic partner, roommate, tenant, client, or other relational partner, as well as criminal, civil, or intelligence suspects. Cybervetting takes advantage of information made increasingly available and easily accessible by regular and popular uses and affordances of Internet technologies, in particular social media. Communication scholars have long been interested in the information seeking, impression management, surveillance, and other processes implicated in cybervetting; however, the uses and affordances of new online information technologies offer new dimensions for theory and research as well as ethical and practical concerns for individuals, groups, organizations, and society.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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