Sosiale mediers rolle i jobbsøkingsprosessen : #Facebook #LinkedIn #Instagram #Snapchat #Twitter
Bibliographic record
Abstract
Sosiale medier er ikke lenger bare en del av den private sfære, men også noe bedrifter bruker aktivt til å promotere seg. Blant annet ser man store muligheter ved bruk av sosiale medier for en rekke HR-funksjoner, inkludert rekruttering. Det vi imidlertid vet mindre om, er arbeidssøkers bruk av sosiale medier i jobbsøking. Denne studien studerer potensielle arbeidstakeres bruk av sosiale medier i jobbsøking. Vi har intervjuet 20 økonomistudenter fra Norge og Canada om deres antakelser om, syn på og praksis rundt bruk av sosiale medier i jakten på sin fremtidige arbeidsgiver. Resultatene viser at informantene i denne studien ikke bruker sosiale medier aktivt i sin orientering om aktuelle arbeidsgivere. Allikevel ser det ut til at eksponering gjennom sosiale medier indirekte påvirker potensielle arbeidstakeres oppfatning av ulike bedrifter. Sosiale medier ser ut til å være mest hensiktsmessig som en del av arbeidsgiveres merkevarebygging for arbeidsgivere (employer branding)-strategi. Rekruttering kan derfor ikke sees på i snever forstand.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.341 | 0.220 |
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".