A token-based local help platform with NLP support
Bibliographic record
Abstract
The motivation behind this thesis arises from the high labor costs commonly observed in Canadian communities, where residents are often forced to acquire multiple skills to cope with everyday needs. A web-based skill-exchange platform—where two people trade services using their respective skills—would be valuable. However, population sparsity often makes matching difficult. To address this challenge, we explore a novel approach to the sharing economy: a local mutual-aid platform. Within this platform, users can consume services provided by others through virtual tokens, while the only way to earn tokens is by offering services themselves. Since these tokens are purely virtual, mutual-aid activities do not incur legal liabilities, nor do they risk creating full-time workers motivated solely by financial profit, which could undermine the spirit of reciprocity. On the implementation side, this thesis leverages an optimized Retrieval-Augmented Generation (RAG) approach to enable query handling under sparse data conditions, ensuring that even limited datasets can yield accurate and explainable recommendations. Furthermore, a self-developed distributed transaction manager based on the Saga pattern ensures the integrity of user data across distributed environments, supporting consistent balance updates, order confirmations, and notifications. The prototype platform we developed demonstrates how combining modern AI techniques with lightweight distributed systems can provide both practical utility and long-term sustainability for local help ecosystems.,
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".