People of Determination (Disabilities) Recruitment Model Based on Blockchain and Smart Contract Technology
Bibliographic record
Abstract
Blockchain technology is an innovative technology that has grown in prominence in recent years that will certainly regulate the development of our network society in the upcoming future. Blockchain technology has received increased care and interest from both academic and general practitioners across the world. Various research articles have been written on the approach, how blockchain technology works and its possible applications in different industries, governmental authorities, etc. Nevertheless, there are no conducted studies that have focused on the usage of blockchain technology in the recruitment process of people of determination (disabilities). This paper aims to establish a POD (People of Determination) platform model. The aim of the model is to support the recruitment process of people of determination (disabilities) by enhancing the chances of them who were hired in different types of United Arab Emirates organizations. To the best of our knowledge, no previous research has been conducted on the usage of blockchain technology in recruitment process of people of determination (disabilities). This research paper will therefore aim to contribute to the existing literature about blockchain technology and recruitment process by providing a proposed model on how to implement the process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".