The human side of the AI layoff story
The curated narrative layer used by analytics. Use as the human sanity check against the charts.
No stories yet
Promoted story count in the selected window. This reflects curated stories, not raw intake.
Heuristic emotional tone across promoted stories. Useful directionally, not as a truth oracle.
Most common response path in the curated dataset: pivoting, gig work, job hunting, or none.
Synthetic stress indicator derived from emotional signals in promoted stories. Directional, not clinical.
Raw intake volume before final curation. This is the staging layer, not the final promoted dataset.
Staged candidates currently allowed through the gate or promoted manually.
Staged candidates blocked from promotion by the collector or by manual moderation.
Most common failure mode in the current review slice. Useful for tuning queries and gates.
Area = promoted story volume. Line = sentiment trend. Use this to compare activity bursts with emotional direction.
Shows how promoted stories respond to pressure: pivots, gigs, job hunting, and other adaptation modes.
Average emotional composition of promoted stories across fear, anger, sadness, and hope.
Shows where the promoted narrative signal is coming from. X is usually noisier than Reddit.
Most frequently named employers in promoted stories, plus rough sentiment toward them.
Shows why staged candidates are failing review or the promotion gate. Useful for tuning the collector.
Accepted vs rejected ratio by source platform. Good for spotting noisy sources.
Moderation layer for staged intake: inspect, filter, promote, or reject with explicit reasons.
| Platform | Decision | Scores | Reason | Flags | Candidate | Actions |
|---|---|---|---|---|---|---|
|
N
D
T
|
|
open source ↗ |
|
Review semantic B1 outputs, inspect evidence spans, and compare bootstrap vs future LLM classifications.
What this dashboard measures, and what it does not.
AI Layoff Pulse tracks the human side of AI-linked job disruption. It is a curated signal system for first-hand narratives about layoffs, displacement, fear, adaptation, and rebuilding.
Use Raw Candidate Review to inspect staged items, filter by platform/decision, and manually promote or reject candidates with explicit reasons. Those reasons help tune the pipeline instead of burying errors under vague manual overrides.