Bibliographic record
Abstract
Background:The impact of the increased popularity of the internet as a platform to search for jobs may benefit every job seeker as an alternative to generate employment opportunities. Graduates that emphasize on general skills have a higher likelihood of disparitywhile searching for jobs. Objectives:This paper explores graduates’ perceptionson the knowledge of how to search for jobs from relevant sourcesr. Methods:The theoretical review focuses on job search strategies, job choices and job accessibility through different sources, highlights the usefulness of job portals for job seekers to find the right job as per their skills and requirements. The existing literature has observed that many job search behaviors through different sourceshas been performed and these behaviors indicate that awareness level affects job seekers’ intentions to apply for jobs. Empirical studies indicate that thechoice of job search by graduates match between a worker’s education and job offered.General skills have a higher likelihood of mismatch at job searches in different countries. Findings:Still in many developing countries, due to lack of awareness of job portals, people are not getting the right jobs and alternatives of their current jobs by different sources. Conclusions:A comprehensive study on applicability of the internet job search is useful for employers, considering the introduction of new graduate recruitment programmers. It is also useful for those wishing to improve their existing ones as well as for institutions of higher education, to reconsider the type of knowledge and skills they provide in order to prepare their students for the real world of work. Implications: Graduates require proper awareness on job search sites and the concerned industry should focus on it as well.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".