Bibliographic record
Abstract
Abstract The demand for talent has increased while the offer has declined and these worrying trends don’t seem to show any sign of change in the near future. According to Bloomberg Businessweek, USA, Canada, UK, and Japan (among many others) will face varying degrees of talent shortages in almost every industry in the coming years. The performed study focuses on identifying patterns which relates to human skills. Recently, with the new demand and increasing visibility, human resources are seeking a more strategic role by harnessing data mining methods. This can be achieved by discovering generated patterns from existing useful data in HR databases. The main objective of the paper is to determine which data mining algorithm suits best for extracting knowledge from human resource data, when in it comes to determining how suited is a candidate for a specific job. First of all, it must be determined a way to evaluate a candidate as objective as possible and rate the candidate with a mark from 0 to 10. To do so, some data sets had to be generated with different numbers of values or different values and wore processed using Weka. The results had been plotted so that it would be easier to interpret. Also, the study shows the importance of using large volumes of data in order to take informed decisions has recently become extremely discussed in most organizations. While finances, marketing and other departments within a company receive data systems and customized analysis, human resources are still not supported by expert systems to process large data volumes. The software prototype designed for the experiment rates individuals (working for the company, or in trials) on a scale from 0 to 10, offering the decision makers an objective analysis. This way, a company looking for talent will know whether the person applying for the job is suited or not, and how much the hiring will influence the overall rating of the department.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".