An Exploratory Study of HBCU Accounting and Other Business Students’ Perceptions and Usage of LinkedIn
Bibliographic record
Abstract
We administer a survey to evaluate accounting and other business students’ perceptions and usage of the social networking site LinkedIn. The participants are students at historically black colleges and universities (HBCUs), who are underrepresented groups. Our research examines how LinkedIn shapes their social identity and establishes their self-presentation in a world of social networking. It also examines how students’ perceptions of LinkedIn benefit their future career development as well as interactive learning. The results of the survey reveal that LinkedIn is an invaluable social media tool for college students to present their social identity, network with professionals as a helpful source of career and job information. However, compared with business students, accounting students put less trust in the information obtained via professional communities on LinkedIn. Accounting students agree that LinkedIn is more distracting than helpful to students for academic work. Our study has strong implications for accounting students and other business students, as well as educators in HBCU settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".