{
  "annotation_id": "225204ba-19ea-4474-8e70-3491a38e6fc6",
  "date_created": "2026-02-17T05:53:05.114000Z",
  "date_modified": "2026-02-17T05:53:05.114000Z",
  "file_hash": "15a2da91b82dfb905faad01fc164a78892128309162faa070488010bf3aa9b75",
  "private": false,
  "record": {
    "abstract": "How many workers displaced by automation can realistically transition to safer jobs? We answer this using a validated knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships (0.74% error rate). While 20.9% of jobs face high automation risk, we find that only 24.4% of at-risk workers have viable transition pathways--defined by $\\geq$3 shared skills and $\\geq$50% skill transfer. The remaining 75.6% face a structural mobility barrier requiring comprehensive reskilling, not incremental upskilling. Among 4,534 feasible transitions, process-oriented skills emerge as the highest-leverage intervention, appearing in 15.6% of pathways. These findings challenge optimistic narratives of seamless workforce adaptation and demonstrate that emerging economies require active pathway creation, not passive skill matching.",
    "arxiv_id": "2601.06129",
    "authors": [
      "Ahmed Dawoud",
      "Sondos Samir",
      "Mahmoud Mohamed"
    ],
    "categories": [
      "cs.CY",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications",
    "url": "https://arxiv.org/abs/2601.06129",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "cb846ca0-0481-4941-a80c-7a5ff27e9923",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}