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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.248 | 0.071 |
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".