{
  "id": 420906,
  "title": "Regarding Peer Review",
  "url": "/competitions/2023-kaggle-ai-report/discussion/420906",
  "author_name": "",
  "post_date": "2023-07-03T06:53:14.772549Z",
  "votes": 1,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hi All, just wanted to know how we have to get the peer review done.<br>\nThanks</p>",
  "messages": [
    {
      "id": "2327769",
      "postDate": "07/03/2023 06:53:14",
      "content": "<p>Hi All, just wanted to know how we have to get the peer review done.<br>\nThanks</p>",
      "rawMarkdown": "Hi All, just wanted to know how we have to get the peer review done.\nThanks",
      "votes": null
    },
    {
      "id": "2328559",
      "postDate": "07/03/2023 17:17:10",
      "content": "<p>My tip for you Varun or anybody else: wrote your impressions about the work that you've read.<br>\n Is it original? Has it creativity? No plagiarism (maybe on another competition, that's an awkward situation. Avoid that!). What you liked on it? What you've learned from it?<br>\nHas it something to be fixed?</p>\n<p>Maybe you could make a search about \"peer review\" and learn something more.</p>\n<p>Keep in mind: Try Not to write as Bot. That's suck and users won't appreciated it. <br>\nWrite from the bottom of your heart, like you're showing a friend what you found about  his work.</p>",
      "rawMarkdown": "My tip for you Varun or anybody else: wrote your impressions about the work that you've read.\n Is it original? Has it creativity? No plagiarism (maybe on another competition, that's an awkward situation. Avoid that!). What you liked on it? What you've learned from it?\nHas it something to be fixed?\n\nMaybe you could make a search about \"peer review\" and learn something more.\n\nKeep in mind: Try Not to write as Bot. That's suck and users won't appreciated it. \nWrite from the bottom of your heart, like you're showing a friend what you found about  his work.",
      "votes": null
    },
    {
      "id": "2330426",
      "postDate": "07/05/2023 02:01:59",
      "content": "<p><strong>You can find the peer-feedback instructions <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 peer-feedback instructions [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": 2328559,
      "author_name": "mpwolke",
      "author_url": "",
      "post_date": "07/03/2023 17:17:10",
      "content": "<p>My tip for you Varun or anybody else: wrote your impressions about the work that you've read.<br>\n Is it original? Has it creativity? No plagiarism (maybe on another competition, that's an awkward situation. Avoid that!). What you liked on it? What you've learned from it?<br>\nHas it something to be fixed?</p>\n<p>Maybe you could make a search about \"peer review\" and learn something more.</p>\n<p>Keep in mind: Try Not to write as Bot. That's suck and users won't appreciated it. <br>\nWrite from the bottom of your heart, like you're showing a friend what you found about  his work.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2330426,
      "author_name": "paultimothymooney",
      "author_url": "",
      "post_date": "07/05/2023 02:01:59",
      "content": "<p><strong>You can find the peer-feedback instructions <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": {
    "2327769": "Hi All, just wanted to know how we have to get the peer review done.\nThanks",
    "2328559": "My tip for you Varun or anybody else: wrote your impressions about the work that you've read.\n Is it original? Has it creativity? No plagiarism (maybe on another competition, that's an awkward situation. Avoid that!). What you liked on it? What you've learned from it?\nHas it something to be fixed?\n\nMaybe you could make a search about \"peer review\" and learn something more.\n\nKeep in mind: Try Not to write as Bot. That's suck and users won't appreciated it. \nWrite from the bottom of your heart, like you're showing a friend what you found about  his work.",
    "2330426": "**You can find the peer-feedback instructions [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"
}