Integrating Natural Language Processing with Expert Systems for Streamlined Evaluation of Applications
Bibliographic record
Abstract
Screening of applications for jobs, education, grants, and the like can often be slow, subjective, and inefficient. To address these issues, a novel framework, the Hybrid AI Platform for Streamlining Evaluation (HAIPSE), is introduced, integrating expert rule-based reasoning with NLP and computer-vision techniques, delivering a structured alternative to traditional applicant tracking systems. The platform’s heuristic scoring module, powered by spaCy, extracts key application details and grades responses against predefined criteria. To capture context and reduce reviewers’ workload, the HAIPSE incorporates large language models, Meta Llama 3-8B and Mistral 8 × 7B, generating concise essay summaries. The novel group-fairness metrics are applied within the evaluation pipeline, making the scoring process more transparent while preserving nuanced content. Furthermore, built-in debiasing steps embed proactive fairness checks directly into the framework’s design, preventing bias rather than merely detecting it post factum. All components of the HAIPSE were trained and tested on a limited set of sample IDs and 2,000+ real applications from the NIB Trust Fund (Canada). Compared with manual reviews, the HAIPSE improves transparency and reduces bias, while a collaborative audit interface bridges AI automation and expert judgment, reinforcing responsible AI. Ethical considerations of fairness and responsible deployment ensure that the resulting assessments remain scalable, interpretable, and equitable.
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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 | 0.032 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".