Job seekers' attitudes toward cybervetting: Scale development, validation, and platform comparison
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 The present research describes the development and validation of a measure of job seekers' attitudes toward cybervetting (ATC). Study 1 involved a sample of participants completing an initial pool of items focusing on one platform (i.e., Facebook) and conducting an exploratory factor analysis. Study 2 included a confirmatory factor analysis and an exploratory structural equation model to establish convergent and discriminant validity. Results of both studies confirmed that the hypothesized three‐factor structure (perceived justice, privacy invasion, and face validity) provided a good fit to the data, explained over 67% of total variance, with all three factors demonstrating high internal consistencies. Study 3 examined the measurement equivalence of the ATC measure, and demonstrated its factor structure and reliability, across four social media platforms (Facebook, LinkedIn, Twitter, and Instagram). Comparing applicants' attitudes across platforms showed significantly more favorable perceptions toward LinkedIn‐based cybervetting than for the other three platforms.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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