We Analyzed 230,392 Records.
Here's What It Means for You.
Workers win appeals when they know the patterns. Denials follow a playbook. We turned four years of tribunal data into tools you can use right now.
How we handle data: ✅ Proven data clearly labelled · ⚠️ Inferred patterns disclosed upfront · 🔢 Win rates are estimated from classified decisions only — 93.9% of all 98,992 WSIAT decisions lack clear outcome keywords, making published rates a model artifact of incomplete public data, not confirmed outcomes · 📖 All code and data open source — See full methodology →
This Is How We Change the System
Every tool we build feeds a cycle that grows stronger with every worker who uses it:
This flywheel is our defensible system — not just UX, but how we close the gap in public data one worker at a time. How to contribute →
💪 Start Here If You Need Help
Injured, denied, or helping someone who is — go straight to the tools. Skip the data.
✅ I Got Denied — I Need to Appeal
You need a strategy now. Start with the guide built from 98,992 real decisions.
🧠 I Want to Understand What's Happening
See the tactics WSIB uses. Know what you're up against before you file.
📚 Browse All Guides & Templates
Musculoskeletal, neurological, legal strategy — 24+ guides built from real cases.
📊 Go Deeper Into the Data
Researchers, policy analysts, advocates with clients — the full dataset and methodology are below.
📈 Interactive Visualizations
5 live charts. Filter, zoom, explore 230,392 records yourself.
View Charts ↓🗄 Raw Data Downloads
122,488 Ontario tribunal decisions + 130,736 employer records. 100% open source. No paywalls.
Download Data →🔬 Deep Pattern Analysis
9-category pattern analysis, CanLII cross-tribunal comparison, and full methodology.
Read Analysis ↓What the Data Actually Shows
3 patterns confirmed across 230,392 records — with disclosed confidence levels and data limitations.
Appeals Work — But WSIB Hides How Often
Confirmed: 98,992 WSIAT decisions analyzed (1987–2026). Of decisions with classifiable outcomes, 726 were allowed and 5,314 denied.
→ Action: Read WSIAT Appeal Guide | Use a Template
"Pre-Existing Condition" Is a Systematic Tactic — Not Bad Luck
Confirmed: 13.3% of analyzed WSIAT cases (2020-2026) involve pre-existing condition as a factor (1,519 cases out of 11,430; 95% CI: 12.7–13.9%). Back/Spine injuries are the most common injury type at 15.3% of all 98,992 decisions.
1 in 8 claims denied this way. If this happened to you, you're not alone — and it's contestable.
→ Action: Recognize the Tactic | Fight Back with Template
Your Employer's Safety Record Is Public — And Searchable
Confirmed: 130,736 Ontario employer safety records analyzed (91,814 NEER + 38,922 CAD-7). Some employers have significantly worse records than others in the same industry.
A documented pattern of incidents at your employer strengthens your claim. This data is yours.
→ Action: Check Employer Safety by City | Download Raw Data
📈 Interactive Visualizations
Explore the Data: 5 Interactive Charts
These visualizations let you filter, zoom, and discover patterns in 230,392 tribunal records. Click any chart to explore.
Data Quality: ✅ All Ontario tribunal data complete (122,488 decisions). 📊 Success rates calculated from real outcomes, not samples. See full methodology →
🏛️ Ontario Tribunal Datasets
Ontario Workers' Rights Data: Four Tribunal Systems Analyzed
We've collected and analyzed decisions from all major Ontario tribunals affecting injured workers, disability benefits, and workplace discrimination cases. Each dataset reveals different patterns in how claims are handled at different stages of the system.
🏛️ WSIAT
Workplace Safety & Insurance Appeals Tribunal
Level: Appeals of WSIB claim denials
Focus: Pre-existing conditions, chronic pain, benefit levels
Success Rate: 60-70% (independent research)
⚖️ HRTO
Human Rights Tribunal of Ontario
Level: Workplace discrimination complaints
Focus: Disability accommodation, discrimination
Outcome Detection: 46-58% from keywords
📋 ONSBT
Ontario Social Benefits Tribunal
Level: ODSP benefit appeals
Focus: Disability benefit eligibility, denials
Data Quality: 100% with metadata
🏢 ONWSIB
Ontario WSIB First-Level Decisions
Level: Initial WSIB claim decisions
Focus: Compare first-level vs appeal outcomes
Status: ⚠️ Collection in progress
🔍 Why Multiple Tribunals? Each tribunal handles different stages of the workers' rights system. WSIB denies at first level (ONWSIB) → Workers appeal to WSIAT → Disability benefits handled by ONSBT → Discrimination cases go to HRTO. Analyzing all four reveals patterns across the entire system, not just one stage.
Cross-Tribunal Insights
- Pre-existing condition denials: 13.3% of WSIAT cases (1,519/11,430) — pattern consistent across tribunals
- Outcome transparency: WSIAT 91.8% unknown, HRTO 46-58% unknown, ONSBT near 100% unknown — systemic data gap
- Appeal success varies by tribunal: WSIAT 60-70%, HRTO unknown, ONSBT unknown (limited outcome data)
- Combined dataset power: 122,488 decisions reveal systemic patterns invisible in single-tribunal analysis
Data Access: All four datasets available for download. View download options & methodology →
🔍 Deep Analysis (For Those Who Want More)
WSIAT Decision Explorer (1987-2026)
98,992 Ontario workers' compensation appeal decisions now available in structured format. The largest open-source WSIAT dataset in Canadian history.
Dataset Overview
| Year Range | Decisions | Metadata Included |
|---|---|---|
| 1987-1999 | 20,208 | DecNum, Date, Keywords, Summary |
| 2000-2009 | 31,928 | DecNum, Date, Keywords, Summary |
| 2010-2019 | 31,691 | DecNum, Date, Keywords, Summary |
| 2020-2026 | 10,772 | DecNum, Date, Keywords, Summary |
| Unknown year | 4,393 | Date field unparseable in source CSV |
| TOTAL | 98,992 | Complete metadata for all |
Open Data Access:
- WSIAT Metadata (JSON) - Complete statistics for 98,992 decisions
- Decisions by Year (41 JSON files) - Organized 1987-2026
- WSIAT Data Documentation - Schema, numbering format, comparison table
- WSIAT vs BC WCAT Comparison - 13.4:1 decision ratio analysis
- Deep-Dive Analysis Report - 9 advanced pattern categories
- Keyword Network Visualization - Interactive graph showing issue relationships
Official Data Source: WSIAT Open Data Portal - CSV export parsed and organized for open research.
WSIAT Pattern Analysis: 40 Years of Insights (1987-2026)
Deep-dive analysis of 98,992 WSIAT decisions reveals patterns in legal issues, workload trends, and representative participation across four decades.
Top Legal Issues (Most Common Keywords)
| Rank | Legal Issue | Cases | % of Total | Description |
|---|---|---|---|---|
| 1 | NEL | 20,680 | 20.88% | Non-Economic Loss (permanent impairment benefits) |
| 2 | Permanent Impairment | 11,841 | 11.96% | Permanent disability assessments |
| 3 | LOE | 10,838 | 10.94% | Loss of Earnings (wage replacement) |
| 4 | FEL | 7,120 | 7.19% | Future Economic Loss |
| 5 | Chronic Pain | 6,876 | 6.94% | Chronic pain syndrome claims |
| 6 | Reconsideration | 6,153 | 6.21% | Requests to reconsider prior decisions |
| 7 | SIEF | 4,654 | 4.70% | Second Injury Enhancement Fund |
| 8 | Right to Sue | 1,763 | 1.78% | Section 31 applications |
Peak Decision Years (Top 5)
- Year 2000: 4,502 decisions (busiest year ever)
- Year 2017: 4,248 decisions
- Year 2018: 3,969 decisions
- Year 2001: 3,844 decisions
- Year 2016: 3,633 decisions
Most Prolific Vice-Chairs (Top 5)
- R. Nairn: 3,860 decisions
- M. Keil: 3,605 decisions
- J. Moore: 2,981 decisions
- V. Marafioti: 2,484 decisions
- S. Ryan: 2,400 decisions
Key Insight: 3,260 unique vice-chairs identified across 40 years. 100% of decisions include vice-chair metadata, enabling workload analysis and consistency tracking.
Full Analysis Report:
- Complete Pattern Analysis Report - All findings, charts, recommendations
- Analysis Data (JSON) - Machine-readable results
Tribunal Evidence Center (April 2026)
We now publish tribunal findings using a strict evidence model: Tier A (confirmed), Tier B (probable), and Tier C (unresolved), with audit confidence intervals.
Four Ontario Tribunals Analyzed (2020-2026)
| Tribunal | Total Cases | Tier A | Tier B | Tier C | Key Finding |
|---|---|---|---|---|---|
| WSIAT Workers' comp appeals (2020-2026 CanLII subset) |
11,430 | 74 (0.6%) | 575 (5.0%) | 10,781 (94.3%) | 73.5% grant rate in 649 classified decisions (Tier A+B). Full dataset: 98,992 decisions (1987-2026). 91.8% of CanLII subset outcomes unresolved. |
| HRTO Human rights complaints |
9,269 | 4,618 (49.8%) | 1 (0.0%) | 4,650 (50.2%) | 73.5% abandonment rate, 70.1% cite email issues |
| ONSBT ODSP/OW appeals |
13,798 | 494 (3.6%) | 3,251 (23.6%) | 10,053 (72.9%) | 67.4% grant rate in classified cases |
| ONWSIB WSIB internal reviews |
431 | 1 (0.2%) | 19 (4.4%) | 411 (95.4%) | 89.5% probable grant rate, very limited data |
Total: 134,920 decisions analyzed (98,992 WSIAT + 35,928 other tribunals). All tribunals use the same tiered evidence framework for transparent outcome reporting.
Open Data Access:
- Strict Evidence Table (JSON) - All four tribunals, A/B/C breakdown
- Tribunal Audit Error-Rate Estimates (95% CI) - Confidence intervals for each tribunal
- Issue Slices Summary - Chronic pain, pre-existing conditions, entitlement denial cross-tribunal analysis
- Connecting the Dots CanLII Keyword Visualization Network - Interactive keyword relationship mapping
Research standard: Tier B is always labeled inferred, and unresolved volume is always disclosed.
📖 Understanding the Numbers (Plain English Guide)
You'll see statistical terms like "95% CI", "χ²", and "p < 0.001" throughout our research. Here's what they mean:
A "margin of error." When we say "20% (95% CI: 17.3-22.7%)", it means we're 95% confident the true number is between 17.3% and 22.7%. Narrower range = more precise measurement.
Tests if a pattern is random or caused by something. Higher number = less likely to be random. Example: χ² = 32.7 vs. critical value = 6.6 means the pattern is NOT random.
The chance this happened randomly. p < 0.001 = less than 1 in 1,000 chance (99.9% certain it's real). p < 0.01 = less than 1 in 100 chance (99% certain). Lower = more confident.
The normal/average percentage across ALL cases. We compare specific injury types to this baseline to see if they're treated differently (e.g., knee 20% vs. baseline 13.3% = bias).
🎯 Bottom Line: These numbers prove patterns are real, not coincidence. When you see "p < 0.001" or "χ² = 32.7", it means: "This is NOT random—something systematic is happening."
🤖 AI-Powered Outcome Predictions: 137,252 Decisions Analyzed
Can You Win? We Analyzed 137,252 Cases to Find Out
Using natural language processing trained on 256,734 decision documents, we've predicted outcomes for every single tribunal decision in our database—not just Ontario, but also BC and beyond. This is the first Canada-wide AI outcome prediction system for workplace and disability tribunals.
Overall Win Rates (All Tribunals Combined)
Win Rates by Tribunal
| Tribunal | Jurisdiction | Total Cases | Win Rate | Most Common Outcomes |
|---|---|---|---|---|
| WSIAT | Ontario Workers' Compensation Appeals | 28,551 | 100% | 28,551 Granted (100%) |
| BCWCAT | BC Workers' Compensation Appeals | 7,916 | 86.4% | 5,772 Granted, 908 Dismissed |
| HRTO | Ontario Human Rights Tribunal | 9,269 | ~varies | 19,228 Abandoned, 1,518 Dismissed - No Violation |
| ONSBT | Ontario ODSP/OW Benefits Appeals | 13,798 | Varies | 41,354 Costs Decisions |
| Other | Mixed Provincial & Local Tribunals | 77,718 | 84.1% | 32,709 Allowed, 6,177 Dismissed |
Most Common Outcomes Across All Cases
ONSBT administrative decisions (30.1%)
Appeals fully granted (34.4%)
Claims allowed (14.5%)
Cases abandoned (14.0%)
Appeals dismissed (3.1%)
HRTO dismissals (1.1%)
What This Means for You
🎯 Key Takeaway: If you've been denied benefits or accommodations and you're considering an appeal, the overall data suggests you have a strong chance of success—but it varies significantly by tribunal.
- WSIAT (Ontario Workers' Comp): 100% prediction rate in our NLP model—but this reflects data limitations, not actual tribunal decisions. Our Tier A+B classification of 649 resolved WSIAT decisions (2020-2026 CanLII subset) shows a 73.5% grant rate. The full dataset outcome gap (93.9% unresolved) prevents a definitive population-level rate.
- BCWCAT (BC Workers' Comp): 86.4% win rate—strong odds if you're prepared with medical evidence.
- Other Tribunals: 84.1% win rate across mixed jurisdictions—consistently high success rates.
- HRTO (Human Rights): High abandonment rate (14% of all cases) suggests procedural challenges—but if you persist, success is possible.
⚠️ Important: These predictions are based on AI analysis of decision text, not official tribunal outcomes. Treat them as indicative patterns, not guarantees. Individual case outcomes depend on evidence quality, legal representation, and specific circumstances.
🔧 API Limitations — Confirmed by CanLII (May 2026): CanLII confirmed directly: "CanLII doesn't provide any data further than what's provided by its API." The API provides case metadata (date, keywords, citation) but no outcome field exists. All 230,392 records were collected via authorized API calls. Outcomes are inferred from keyword patterns in decision text — our NLP model predicts unknown outcomes with 79% accuracy based on case keywords and patterns. To get 100% accurate outcomes would require manually reading each case individually.
How We Built This (Methodology & Transparency)
Training Data
- 256,734 labeled examples from 105 tribunal decision files
- 14 outcome categories: Granted, Allowed, Dismissed, Denied, Abandoned, Reconsideration, Allowed - Violation Found, Dismissed - No Violation, Costs Decision, Interim Decision, Settled, Withdrawn, No Jurisdiction, Deferred
- Natural Language Processing: Naive Bayes classifier trained on decision keywords, issue descriptions, and tribunal metadata
- Test accuracy: 79.0% on 3,756 held-out examples (industry-standard train/test split)
Confidence Levels
- High confidence (≥80%): 72.1% of predictions (25,213 decisions) - deployed in search results
- Medium confidence (60-79%): Shown with warning label
- Low confidence (<60%): Not deployed, flagged for manual review
Data Sources
- Outcome Summary (JSON) - Overall statistics
- Outcome by Tribunal (JSON) - Tribunal breakdowns
- Outcome by Year (JSON) - Temporal trends 2020-2026
- All raw decision files: Available in /data/tribunal-decisions/ directory
Open Source Commitment: All outcome prediction data is publicly available. We publish our methodology, confidence scores, and accuracy metrics so you can evaluate the reliability yourself.
Using Outcome Predictions in the App
When you search for tribunal decisions in the 3mpwrApp, you'll now see outcome badges on every case:
Worker won
Worker lost
Mixed outcome
Sent back for reconsideration
Filter by outcome: Search for "chronic pain" + "Allowed" to find winning precedents. Compare similar cases: See how your situation matches cases that succeeded.
�📚 Knowledge Base & Resources
All guides and templates below are derived from analyzing 11,430+ tribunal decisions. These are not generic advice—they’re evidence-based strategies from actual winning cases.
Injury-Specific Guides (16 Comprehensive Articles)
WSIB Claim Guides: What Actually Works
Each guide analyzes hundreds of tribunal decisions to show you exactly what evidence wins claims for your specific injury type. No generic advice—these are patterns from real cases.
Based on: 11,430 total analyzed cases (2020-2026 WSIAT decisions). All guides live now: 19 comprehensive injury-specific guides + 5 legal strategy guides available above.
Appeal Letter Templates (50+ Fill-in-the-Blank Letters)
Ready-to-Use Appeal Templates
Professional appeal letters you can customize in 30 minutes. Each template includes:
- Legal arguments from winning cases (exact language that worked)
- Medical evidence checklist (what documents to attach)
- Employer evidence pushback (how to counter their claims)
- Timeline walkthrough (step-by-step what happens next)
📄 Featured Templates (Live Now!)
Professional-grade fill-in-the-blank templates · Addresses all common denials · Free to use
🔜 More Templates Being Added
Additional templates for shoulder, knee, mental health/PTSD, carpal tunnel, concussion, fibromyalgia, hearing loss, herniated disc, impairment rating, neck injury, respiratory, rotator cuff, strain/sprain, tendinitis, and more are currently being converted from JSON data to user-friendly markdown templates.
Currently stored as structured JSON data format. Watch this space for updates.
🔄 Close the Loop: How Your Feedback Makes Data Better
This is the key: Research only works if it cycles back to action. Here’s how you accelerate the flywheel.
The 3mpwr Feedback Cycle
📊 Data → 📖 Patterns → ✅ Tools → 💪 You Win → 🔄 You Share → 📊 Better Data
Step 1: We Analyze Data
230,392 records analyzed. Patterns detected (pre-existing = 13.3%, knee bias = 20%). Tactics identified. Statistics calculated.
Step 2: We Build Tools
Guides written. Templates created. Visualizations built. All based on real patterns from real decisions.
Step 3: You Use Tools
Read the guides. Use the templates. Fight your appeal. Our data shows 73.5% grant rate in resolved WSIAT decisions — and most workers never even appeal.
Step 4: You Share Outcome
Win or lose, share your result. That fills the 91.8% outcome gap. The next worker gets better data.
Step 5: Cycle Accelerates
More outcomes = better patterns = stronger tools = more wins = richer data. The flywheel spins faster.
How You Can Contribute:
- Use the tools → Fight your case with real data
- Share your outcome → Email empowrapp08162025@gmail.com (anonymous OK)
- Report new tactics → Help us detect emerging patterns
- Challenge our methodology → Find errors? Tell us. We fix it.
→ Start the cycle: Use the Evidence Locker to upload your denial letter → Get personalized strategy → Win your appeal → Share result → Help next worker
🔜 Coming Soon
Human Rights Tribunal Decision Network
Ontario Human Rights Tribunal (OHRT) Pattern Analysis
Analyzing disability discrimination cases, settlement patterns, and systemic barriers. Expanding beyond workers' compensation to cover employment discrimination, housing, and services.
Estimated Launch: Summer 2026 | Expected Dataset: 5,000+ decisions
Employment Standards Tribunal Visualization
Employment Standards Decisions & Wage Theft Patterns
Tracking unpaid wages, termination disputes, and employer violations. Cross-reference with WSIB claims to identify employers systematically denying rights.
Estimated Launch: Fall 2026 | Expected Dataset: 8,000+ decisions
Cross-Tribunal Comparison Tool
Multi-Tribunal Pattern Detector
Compare outcomes across WSIB, Human Rights, Employment Standards, and Landlord-Tenant tribunals. Identify workers caught in multiple systems, systematic employer bad actors, and regional disparities.
Estimated Launch: 2027 | Requires: All Ontario tribunals collected
Canada-Wide Workers’ Compensation Network
Provincial Comparison: BC, AB, QC, NS, MB, SK
Expand WSIB visualization to cover WorkSafeBC, WCB Alberta, CNESST (Quebec), and all provincial systems. Compare denial rates, appeal success, and systemic patterns across Canada.
Estimated Launch: 2027-2028 | Expected Dataset: 50,000+ decisions
📊 Research Methodology
All tools on this page follow these principles:
- Open Source Code
- Analysis scripts published on GitHub
- Reproducible methodology
- Community contributions welcome
- Transparent Data Sources
- CanLII (Canada’s free legal database)
- Provincial tribunal websites
- Freedom of Information Act requests where necessary
- No paywalls, no corporate databases
- Accessible Visualization
- WCAG 2.1 AAA compliant
- Keyboard navigation
- Screen reader compatible
- Color-blind friendly palettes
- Community-Driven
- Workers can submit case outcomes to fill data gaps
- Injured worker advocates review methodology
- Thunder Bay & District Injured Workers Support Group partnership
🤝 Contribute to Research
For Injured Workers
- Share Your Case Outcome (anonymous): Email empowrapp08162025@gmail.com
- We’ll add to our database to fill the 91.8% outcome gap
- Help future workers understand success rates
For Researchers & Advocates
- Validate Our Methodology: Review our analysis scripts
- Suggest New Visualizations: What patterns should we look for?
- Provide Data: Do you have tribunal datasets we’re missing?
For Developers
- Improve Visualizations: Fork our D3.js code and submit pull requests
- Build New Tools: Expand to your province/tribunal
- Optimize Performance: Help us scale to 100,000+ decisions
📊 Our Research Standards: Credibility Over Sensationalism
Why Trust Our Analysis? We’ve analyzed 11,430+ tribunal decisions using rigorous statistical methods. But we distinguish facts (what data proves) from interpretations (what patterns suggest).
What We Can PROVE:
✅ 11,430 WSIAT decisions analyzed (2020-2026, 95%+ coverage of all tribunal cases) ✅ 91.8% missing outcome metadata (10,491 cases have no win/loss categorization in CanLII) ✅ Statistical anomalies detected (July 2023: 39 decisions vs. 154 average, Z = -2.94, p = 0.003) ✅ Body part bias measured (knee injuries = 20% (95% CI: 17.3-22.7%) “pre-existing” denial rate vs. 13.3% (95% CI: 12.7-13.9%) baseline, χ² = 32.7, p < 0.001) ✅ Delay tactics quantified (reconsideration adds 2.0 years vs. 0.5 for direct appeals)
What We INFER (with caveats):
🔍 Systematic patterns suggest:
- Dysfunction or deliberate cost-shifting (financial incentives + historical precedent align)
- Alternative explanations (incompetence, understaffing, pandemic) considered but less likely
- We CANNOT prove intent without internal WSIB documents
Statistical Methods Used:
- Anomaly detection (Z-score analysis, p-values)
- Co-occurrence networks (which denial tactics cluster together)
- Temporal trend analysis (patterns over time)
- Chi-square tests (body part bias, keyword associations, fiscal year-end spike)
- Confidence intervals (all proportions reported with 95% CIs using formula: p ± 1.96 × √(p(1-p)/n))
- Effect sizes (Cohen’s h for proportional differences)
- Bonferroni correction (for multiple testing)
- Sensitivity analysis (robustness to missing data)
Data Transparency:
✅ All code open source: GitHub: 3mpwrapp.github.io
✅ Raw data public: tribunal-decisions/
✅ Community review welcomed: Find errors? Email empowrapp08162025@gmail.com
✅ Replication instructions: Run scripts/scrape-onwsiat.mjs + scripts/analyze-onwsiat-ultra-deep.mjs
Limitations We Acknowledge:
⚠️ We DON’T have:
- True worker win rates (91.8% missing outcomes)
- WSIB internal policy documents
- Adjudicator performance data
- Regional success rate breakdowns
- Representation impact (only 3.6% of cases mention lawyers)
✅ We DO have:
- Complete keyword patterns (13,000+ keyword occurrences)
- Temporal trends (6 years of monthly volumes)
- Body part bias rates (shoulder, knee, back, etc.)
- Delay measurements (reconsideration vs. direct appeal)
- Co-occurrence networks (which tactics appear together)
Full methodology available in blog posts below (see “Methodology & Evidence Standards” sections)
🔗 How Research Drives Action (3mpwrApp Flywheels):
Pattern Detection (11,430 cases)
↓
Knowledge Base (16 injury guides: what evidence wins)
↓
Appeal Templates (50+ fill-in-blank letters)
↓
Community Support (workers share outcomes → close 91.8% data gap)
↓
MORE DATA (feedback loop improves research)
You can help close the data gap:
- 📊 Share your outcome (anonymous): Won/lost, injury type, how long it took
- 📢 Spread awareness: Share visualizations, templates, guides with injured workers
- 🔍 Challenge us: Find errors in our analysis? We want to know
- 🤝 Join community: Email to connect with other workers fighting same battles
📚 Related Blog Posts
- WSIB Exposed: Statistical Evidence Reveals Systematic Patterns - Rigorous analysis of 11,430 cases
- The WSIB Black Box: 1.14-2.29M Workers Suppressed - 91.8% outcome obscurity + suppression research
- Hidden Language of Denial: WSIB Keyword Patterns - Decode your denial letter
- Building Canada’s Legal Database from Cold Start - Data collection methodology
📧 Questions or Feedback?
- Email: empowrapp08162025@gmail.com
- Mastodon: @3mpwrApp@mastodon.social
- Bluesky: @3mpwrapp.bsky.social
All research tools are provided free of charge, with open source code and transparent methodology. This is community-driven transparency for workers’ justice.