The role of social media in influencing career success
Bibliographic record
Abstract
Purpose – The purpose of this paper is to help us better understand if it is beneficial for individuals to use social networking sites (SNSs) to expand their networking opportunities, translating into greater career success. A significant key to career success is networking. SNSs are changing the way employees develop their networks with businesses and with other individuals. Design/methodology/approach – This study uses archival data including academic records for 1,182 accounting alumni from a large Canadian public institution. This dataset was expanded by obtaining social network information (presence and use) for each individual’s record. Findings – After controlling for a number of indicators of career success, the study found that presence on SNSs such as LinkedIn and the amount of activity therein has a strong and consistent association with metrics of professional success not found with non-professional sites such as Facebook, Twitter and MySpace. Originality/value – This study provides empirical support for the value of social networking as a proxy for the development of social capital. Support is in establishing the link between a group of social network profile characteristics and metrics of one’s career success. Distinguishing LinkedIn as chiefly connecting to alumni successes may be reflected in the weights attached to the profile characteristics as opposed to information coming from other sources.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".