global-layoffs-workforce-interpreter

Global Layoffs & Workforce Interpreter

Author: Hussein Adeiza (mabera) Role: Licensed Environmental Health Officer, Abuja Nigeria Base Model: Mixtral 8x7B Fine-tuned with: AutoScientist by Adaption Labs

Model Description

A LoRA adapter fine-tuned to interpret real global tech layoffs data, producing structured analytical reasoning grounded entirely in directly-downloaded statistics from a real tracked dataset (layoffs.fyi). This submission completes all 7 categories of the AutoScientist Challenge 2026 Part 2.

A Note on Subject Matter

This dataset addresses real workforce reductions affecting real people's livelihoods. All interpretation stays measured and analytical, including honest disclosure of data limitations (missing figures, likely reporting bias) rather than overstating confidence in the underlying figures.

Training Data

• Source: layoffs.fyi tracked data, accessed via a properly-cited GitHub repository, downloaded directly, 2,361 real company entries (2020-2023)
• Dataset: 5 original prompt-completion pairs, every number computed via pandas directly from the raw source file, expanded via Adaptive Data (no recipe modifications, full 20K+ datapoint expansion)
• Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/global-layoffs-and-workforce-interpreter

Training Metrics

• Win rate (on dataset): 70% adapted vs 30% base model
• Base model: mistralai/Mixtral-8x7B-Instruct-v0.1
• Method: LoRA, no recipe modifications
• Dataset quality: 8.0 → 8.6 (+7.5% relative improvement, Grade B)
• Percentile: 31.5
• Domain classification: Data-analysis-visualization (60%) / Market- analysis (20%) / Corporate-business (20%), not a clean HR match; disclosed here as this submission was intended for the HR category

Key Cited Findings (from the raw downloaded source only)

• Global tracked layoffs followed a non-monotonic trend: 81,068 in 2020 (COVID shock), falling to 15,823 in 2021 (recovery), then rising to 161,711 in 2022 and 127,277 in the first quarter of 2023 alone
• Funding raised shows almost no correlation with layoff scale (r = 0.077 across 1,491 companies with complete data), challenging the intuitive assumption that better-funded companies cut more
• Post-IPO (mature, publicly-traded) companies account for 53.1% of total tracked layoffs, likely reflecting company scale rather than instability
• Roughly a third of entries (31.3% missing headcount, 33.2% missing percentage) lack key figures, meaning aggregate totals are conservative lower bounds, not precise counts
• The dataset's heavy US concentration (66% of the geographic total) most likely reflects reporting bias in the underlying tracker toward English-language tech media, not a true global distribution

Credits

Powered by Adaptive Data — Adaption Labs AutoScientist Challenge 2026, Part 2 — HR Category