{
  "id": 421583,
  "title": "2023 Kabyle AI Report",
  "url": "/competitions/2023-kaggle-ai-report/discussion/421583",
  "author_name": "",
  "post_date": "2023-07-05T23:21:20.127998600Z",
  "votes": null,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hello Kaggle, when do we get to review the other teams  three reports for peer review </p>",
  "messages": [
    {
      "id": "2332017",
      "postDate": "07/05/2023 23:21:20",
      "content": "<p>Hello Kaggle, when do we get to review the other teams  three reports for peer review </p>",
      "rawMarkdown": "Hello Kaggle, when do we get to review the other teams  three reports for peer review",
      "votes": null
    },
    {
      "id": "2332042",
      "postDate": "07/05/2023 23:38:53",
      "content": "<p><strong>You can find the instructions for providing peer-feedback <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions.\" target=\"_blank\">here</a>:</strong></p>\n<ul>\n<li>Step 1: Wait until the July 5 deadline when all of the notebooks (containing the essays) are made public</li>\n<li>Step 2: Navigate to <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/code\" target=\"_blank\">https://www.kaggle.com/competitions/2023-kaggle-ai-report/code</a></li>\n<li>Step 3: Find a notebook that was published on the same topic as what you wrote your essay about, but that does not yet have 3 comments leaving constructive criticism. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.</li>\n<li>Step 4: Leave a comment containing constructive criticism, according to the guidelines described <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions\" target=\"_blank\">here</a>. </li>\n</ul>\n<p><strong>Guidance for providing high quality peer-feedback:</strong></p>\n<ul>\n<li>Provide an accurate summary of the notebook being reviewed</li>\n<li>Assess whether appropriate context was set, and that all necessary<br>\nreferences were cited</li>\n<li>Evaluate the overall quality of the writing and the composition of the<br>\nwork</li>\n<li>Evaluate the significance of the work and interest to the ML community</li>\n<li>Verify the correctness of the claims made within the essay</li>\n<li>Provide feedback concerning potential flaws for any unsupported claims</li>\n<li>Assess whether there exists content that might be considered inappropriate (e.g.<br>\nplagiarism, academic misconduct, or incorrect statements)</li>\n</ul>\n<p>Hopefully that helps!</p>",
      "rawMarkdown": "**You can find the instructions for providing peer-feedback [here](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions.):**\n- Step 1: Wait until the July 5 deadline when all of the notebooks (containing the essays) are made public\n- Step 2: Navigate to https://www.kaggle.com/competitions/2023-kaggle-ai-report/code\n- Step 3: Find a notebook that was published on the same topic as what you wrote your essay about, but that does not yet have 3 comments leaving constructive criticism. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\n- Step 4: Leave a comment containing constructive criticism, according to the guidelines described [here](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions). \n\n\n**Guidance for providing high quality peer-feedback:**\n - Provide an accurate summary of the notebook being reviewed\n - Assess whether appropriate context was set, and that all necessary\nreferences were cited\n - Evaluate the overall quality of the writing and the composition of the\nwork\n - Evaluate the significance of the work and interest to the ML community\n - Verify the correctness of the claims made within the essay\n - Provide feedback concerning potential flaws for any unsupported claims\n - Assess whether there exists content that might be considered inappropriate (e.g.\nplagiarism, academic misconduct, or incorrect statements)\n\nHopefully that helps!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2332042,
      "author_name": "paultimothymooney",
      "author_url": "",
      "post_date": "07/05/2023 23:38:53",
      "content": "<p><strong>You can find the instructions for providing peer-feedback <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions.\" target=\"_blank\">here</a>:</strong></p>\n<ul>\n<li>Step 1: Wait until the July 5 deadline when all of the notebooks (containing the essays) are made public</li>\n<li>Step 2: Navigate to <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/code\" target=\"_blank\">https://www.kaggle.com/competitions/2023-kaggle-ai-report/code</a></li>\n<li>Step 3: Find a notebook that was published on the same topic as what you wrote your essay about, but that does not yet have 3 comments leaving constructive criticism. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.</li>\n<li>Step 4: Leave a comment containing constructive criticism, according to the guidelines described <a href=\"https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions\" target=\"_blank\">here</a>. </li>\n</ul>\n<p><strong>Guidance for providing high quality peer-feedback:</strong></p>\n<ul>\n<li>Provide an accurate summary of the notebook being reviewed</li>\n<li>Assess whether appropriate context was set, and that all necessary<br>\nreferences were cited</li>\n<li>Evaluate the overall quality of the writing and the composition of the<br>\nwork</li>\n<li>Evaluate the significance of the work and interest to the ML community</li>\n<li>Verify the correctness of the claims made within the essay</li>\n<li>Provide feedback concerning potential flaws for any unsupported claims</li>\n<li>Assess whether there exists content that might be considered inappropriate (e.g.<br>\nplagiarism, academic misconduct, or incorrect statements)</li>\n</ul>\n<p>Hopefully that helps!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2332017": "Hello Kaggle, when do we get to review the other teams  three reports for peer review",
    "2332042": "**You can find the instructions for providing peer-feedback [here](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions.):**\n- Step 1: Wait until the July 5 deadline when all of the notebooks (containing the essays) are made public\n- Step 2: Navigate to https://www.kaggle.com/competitions/2023-kaggle-ai-report/code\n- Step 3: Find a notebook that was published on the same topic as what you wrote your essay about, but that does not yet have 3 comments leaving constructive criticism. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\n- Step 4: Leave a comment containing constructive criticism, according to the guidelines described [here](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions). \n\n\n**Guidance for providing high quality peer-feedback:**\n - Provide an accurate summary of the notebook being reviewed\n - Assess whether appropriate context was set, and that all necessary\nreferences were cited\n - Evaluate the overall quality of the writing and the composition of the\nwork\n - Evaluate the significance of the work and interest to the ML community\n - Verify the correctness of the claims made within the essay\n - Provide feedback concerning potential flaws for any unsupported claims\n - Assess whether there exists content that might be considered inappropriate (e.g.\nplagiarism, academic misconduct, or incorrect statements)\n\nHopefully that helps!"
  },
  "source": "meta"
}